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1. Frame number intervals in which Type of traffic visible in this Interval length in foreground objects are visible interval frames 421 463 Car 42 647 690 Car 43 777 864 Car and cyclist 87 1023 1107 Cyclists 84 1464 1579 Cyclist 115 2066 2110 Car 44 2213 2264 Car 51 2441 2541 Car and cyclist 100 2634 2672 Car 38 3563 3890 Pedestrian cyclists and a car 327 4190 4233 Car 43 4316 4386 Cyclist 70 4485 4513 Motorcycle 28 Figure 15a ground truth results for video A which has a total of 4581 frames and 1072 frames in which foreground objects were visible Frame number intervals in which Type of traffic visible in this Interval length in foreground objects are visible interval frames 89 170 Car and cyclist 81 321 386 Cyclist 65 697 813 Cyclists 116 887 921 Car 34 999 1251 Car and cyclists 252 1265 1453 Car and cyclists 188 1473 1503 Car 30 1622 1648 Car 26 2205 2445 Cyclists 240 2538 2572 Car 34 2622 2705 Cyclist 83 2843 3319 Car cyclist and pedestrian 476 3400 3476 Cyclist 76 3794 3876 Cyclist 82 3895 4117 Pedestrian and a dog 222 Figure 15b ground truth results for video B which has a total of 4163 frames and 2005 frames in which foreground objects were visible 33 Frame number intervals in which Type of traffic visible in this Interval length in foreground objects are visible interval frames 190 258 Car 68 615 659 Car 44 797 837 Car 40 1111 1155 Car 44 1363 1409 Car 46 1734 1777 Car 43 2058 2508 Car pedestria
2. The deterministic approaches can be further subdivided Model based deterministic approaches rely on the notion that the luminance ratio the ratio of the intensity value of 13 a pixel when it is under shadow to that when it is under illumination is a constant 24 A linear transformation is used to describe the reduction of pixel intensity in shadow regions When a new image frame comes in its pixels with illumination reductions that follow the linear model are then identified as probable shadow pixels Non model based deterministic approaches use a comparison between the current image frame I x y and the background image frame obtained from an arbitrary model B x y and then set a threshold to classify a pixel as a shadow or non shadow pixel for example the Sakbot system 10 which works in the HSV colour space uses the following equation to determine if a pixel is a shadow pixel I wy ype SP x y B x y 0 otherwise lt BAC x y BS xy ST All x y BY xy ST 11 where x y and B x y with c H V S is the hue saturation and value component of the pixel In this equation takes into account the power of the shadow that is how strong the light source is with regard to the reflectance of the objects So in sunny outdoor scenes a low value for a should be set J prevents that background changes due to camera noise are not classified as shadow T and T are thresholds for the saturation an
3. Figure 16a results of the deterministic and statistical background subtraction algorithms applied on video A together with the ground truth intervals Value of parameter Rb 1200 Recorded frame intervals using the statistical Frame number intervals in which foreground objects are Recorded frame intervals using the deterministic approach visible ground truth approach 89 170 2205 2445 90 110 2236 2431 88 192 2622 2704 321 386 2538 2572 132 169 2542 2579 325 384 2743 2769 697 813 2622 2705 353 385 2622 2700 697 812 2785 3011 887 921 2843 3319 735 812 2839 3007 887 921 3020 3131 999 1251 3400 3476 889 929 3022 3125 999 1249 3139 3162 1265 1453 3794 3876 1008 1256 3400 3434 1271 1450 3195 3304 1473 1503 3895 4117 1292 1445 3458 3477 1473 1502 3400 3490 1622 1648 1473 1498 3797 3875 1621 1647 3795 3875 1624 1648 3900 4124 2206 2436 3895 4114 2537 2570 Figure 16b results of the deterministic and statistical background subtraction algorithms applied on video B together with the ground truth intervals Value of parameter Rb 250 Frame number intervals in Recorded frame intervals using Recorded frame intervals which foreground objects are the deterministic_approach using the statistical visible ground truth approach 190 258 8 140 1111 1155 19 20 797 838 615 659 156 1201 42 46 948 797 837 190 412 1359 1408 51 65 1009 1111 1155 536 1588 1589 107 1107 1155 1363 1409 615 659 1736 1776 156 157 1201 1734 1777 780 1867 1868 191 261 13
4. if Sp lt 1 and S gt T then the current pixel is a candidate shadow pixel A final check is needed with respect to colour distortion This is done by first calculating the square distance vector between vector c and S pap lt Dsg pel 22 and then classifying the pixel as shadow pixel if and only if D lt mog Spee 23 where m is the squared Mahalanobis distance which was described in chapter 5 2 o is the variance of the current Gaussian component In appendix B equations 25 and 26 are rewritten for optimized implementation and also graphically is shown why this condition should hold Update the mask at corresponding location write value 125 break repeat else the pixel is a pure foreground pixel continue with the next Gaussian component e If all Gaussian components are processed and the pixel is still not classified as a shadow pixel classify the pixel as a pure foreground object update the mask at corresponding location write value 255 24 5 4 Video Summarization For both background subtraction implementations a video summarization option is included With this option enabled which can be activated with the R or Rb parameter see chapter 5 5 video frames which includes enough moving objects are written to a video file on disk The idea behind this feature is that frames which do not contain enough movement are not recorded This results in a video file which only contains movements of interest and e
5. Save Preset button and reloaded by using the Load Preset button The Test Camera button corresponds to the clean parameter which will just show the incoming unprocessed video frames Enabling the shadow detection option in the GUI corresponds to the setting of parameter s to value 1 The use pre and postrecording technique option enables the extended video summarization described in section 5 4 and displayed in figure 12 6 Evaluation In this chapter the two implemented background subtraction methods described in chapter 5 are evaluated using recorded movie files The movies were recoded with a Philips ToUcam Pro webcam In 6 1 the characteristics of the movies are described in 6 2 we discuss for each of the two algorithms which parameter settings are optimal given the types of videos In section 6 3 a comparison measure is proposed to actually compare the two subtraction methods Finally in 6 4 both implemented algorithms are compared using a given performance measure 2I Surveillance Monitor Using Background Subtraction fel Ka paaa a maa oi LI lt paste file path here gt Figure 11 GUI for easy control of both background subtraction methods and their parameters 28 Input Parameter R The recording star threshold the number of foreground pixels that should be present to record the frame obtain next binary number of foreground pixels exceeds the recording reshok
6. A sudden illumination change also occurs at the moment when the street lights are turned off and on o Weather conditions like snow fog or rainfall causes more noise in the video stream and makes the movement of the objects less detailed o Since we have an urban setting not only cars and trucks will drive on the roads but also more versatile vehicles like bikes and scoot mobiles could be present Also animals and pedestrians might cross the roads o Not only vehicles and pedestrians move in the scene but also environmental movements depending on wind speed like branches of nearby trees flags debris on the road moving commercial billboards etc o Vehicles can become part of the background when for example a car is parked in the scene o In general urban intersections are very well lit So detecting objects at night times should not be a more difficult operation then detecting objects at daytimes 2 2 The tasks and requirements of a robust urban traffic monitor system Given the setting in the previous section the requirements of an urban traffic monitor system can be determined First of all the system should have an adaptive background model since we have to cope with sudden changes of illumination in the scene and objects can become part of the background Secondly since queuing of vehicles does occur often the detection algorithm should successfully detect all separate vehicles even if they are very close to each other which
7. Special also bikers and pedestrians Figure 13 Three video sequences recorded with a webcam that will be used for testing 6 2 Parameter tuning In this section the optimal values for the parameters discussed in 5 5 will be determined for both implementations given the urban traffic setting Deterministic implementation The essential parameters in this implementation are the number of frames to create the initial background model N the subtraction threshold T whether to use the mean or the 30 median to create the initial background model I the threshold r for the shadow darkness s and finally the size of the history buffer used by the shadow detection algorithm Sf For the best performance of the deterministic implementation it is necessary to have a number of frames of the scene without any traffic present To capture as much information as possible for creating the initial background model the number of frames N should be set till that frame number in which foreground objects start appearing in the scene So when the first object comes in the scene at frame 100 N should be set to 99 Directly related to the value of N is the question whether the mean 1 0 or the median 1 1 should be used to construct the background see section 5 1 In all the videos it can be seen that we have a lot of moving background objects caused by the wind like moving tree branches Therefore for a number of pixels in the scene outlier value
8. approach number of frames Video A 85 2 88 4 4581 Video B 88 6 94 6 4163 Video C 83 5 93 4 3024 Figure 17 performance of both subtraction algorithms on the three test videos From the scores we can see that the statistical approach has a better performance in all cases No matter the weather conditions the statistical approach outperforms the deterministic approach Furthermore we see that wind is the most difficult problem for the subtraction algorithms In video A a lot of trees and branches are moving in the 35 wind A good example can be seen in figure 19 In the graph of video A interval 241 482 it can be seen that a lot of frames are recorded both algorithms in which actually no foreground objects are visible Also from figure 19 it can be seen for video A that the deterministic approach makes more misclassifications caused by the wind for example in the interval 2652 3616 In the scene with the sunny weather condition video B it can be seen that both algorithms perform very well The cast shadows were eliminated successfully by the shadow detector In the scene with the rainy weather condition video C it can be seen that there are a number of small misclassification intervals These are caused by falling raindrops visible in just a few frames resulting in big trails in front of the camera As can be seen from figure 19 the statistical algorithm produces a lot of those short misclassified frame intervals especia
9. background components Furthermore the shadow detector works very well in the test scenes as can be seen in the videos showing the subtraction results The software developed works in real time using a webcam stream 640x480 or an arbitrary movie file as input Using the webcam the system can be used as a smart surveillance system where only frames are recorded when movement detected by one of the two implemented subtraction algorithms is present 37 8 Future work This research aimed at the background subtraction part of an urban traffic monitoring system As displayed in figure 2 a lot of other components are needed for the proposed system The binary mask that separates the background from the foreground is the result of the subtraction algorithms in this research This data should be used by a tracking system that is able to track each object of interest in the scene separately Furthermore it would be nice if the tracker system is able to detect the same vehicle in different scenes In that way the actual flow of each vehicle in the city can be monitored Having the tracking results traffic parameters like speed density and classification of a vehicle can be extracted and stored Proposed improvements within the background subtraction algorithm are to prevent queuing vehicles to be counted as background objects if those vehicles are not moving for a period of time A possible solution is to select regions in the scene where newly detecte
10. by observing that these Gaussians have the most supporting evidence and lowest variances 26 Therefore the authors order the K distributions in the mixture by the value of w o This value increases both as a distribution gains more evidence and as the variance decreases Then finally the first B distributions are chosen as the background model where 20 b B argmin X gt T 19 k 1 The value of T determines the minimum portion of the data that should be accounted for by the background When higher values for T are chosen a multi model background model is formed which can handle repetitive background motion The limitations of the system described above is that the maximum number of Gaussian components per pixel is low and a low weight component is only deleted when this maximum number of components is reached The implementation of the statistical background model in our research is analogue to the method described by Zivkovic see 28 which overcomes these limitations An important extension in the work of Zivkovic with respect to Stauffer et al See 26 is the introduction of automatically selecting the number of required distributions K Initially the system starts with just one component in the GMM centred on the first sample Zivkovic looks at the prior evidence for a class c which is the number of samples that belong to that class a priori Using this information it is possible to exclude classes from the set of distribu
11. Instead it recursively updates a single background model based on each incoming frame These techniques are used a lot in literature see 15 16 17 26 27 28 Recursive techniques require less storage than non recursive techniques but an error that might occur in the background model will be visible for a long time Now three representative recursive techniques for background modelling found in literature will be described shortly o Approximated median filter Since the good results that can be obtained using non recursive median filtering a approximated recursive variant was proposed This method is particularly interesting since it is reported that this method is successfully applied in an traffic monitor system 17 With this approach the running estimate of the median is incremented by one if the input pixel is larger than the estimate and decreased by one if the pixel is smaller than the estimate This estimate converges to a value for which half of the input pixels are larger than and half are smaller than this value which is exactly the same that we get when calculating the median in the non parametric approach o Kalman Filter In its most general form a Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete or noisy measurements Applied to background modelling it should estimate the state of the current background model The Kalman filter should learn from its e
12. This area can be very large especially in the sunset and sunrise situations see figure 3 A cast shadow can be further classified in umbra and penumbra When an object is fully opaque so all the light is blocked directly by the object the cast shadow that occur is a umbra When the object is partially transparent only a certain amount of light is blocked and the cast shadow that occurs is a penumbra Looking at the setting of traffic monitoring and its segmentation step it can be seen that cast shadows are important to eliminate Self shadows should not be eliminated since they will result in incomplete object silhouettes 25 Figure 3 cast shadows can overlap other foreground objects shadow elimination techniques statistical deterministic approaches approaces non non model parametric based parametric Figure 4 Types of shadow elimination techniques as stated in 10 In figure 4 an hierarchy of shadow elimination techniques in literature is displayed The two main approaches often taken are the deterministic approach 7 8 12 14 24 and the statistical approach 9 11 25 In the former approach the decision is just a binary decision process a pixel just belongs to the foreground object or it belongs to its cast shadow In the latter approach uncertainty is added by using probabilistic functions to describe the class membership Adding uncertainty to the class membership assignment can reduce noise sensitivity 13
13. bounding box are repeated for each pixel x y in the current frame I When the mask for the current frame is Link to the C library used for the implementation of the statistical approach http staff science uva nl zivkovic Publications CvBSLib0_9 zip 21 created that is when all pixels of current frame I are processed the algorithm continues at the arrow pointing out of the red bounding box construct binary mask Mi x y fill with zeros video frame Iix y available For each pixel in create a new gaussian distribution does lx y fall inside an with initial existing distribution parameters Update the parameters of all create the first distributions using gaussian distribution equations 20 21 with initial parameters and 23 the weights of all the stribution are gt 0 normalize the weights of the K distributions sum up to 1 determine which components B belong to background using equation 19 Set corresponding pixel in mask M to 1 foreground urrent pixel value lies in B pixel in mask M to 0 background continue if all pixels in mask are set Figure 9 flowchart of the background subtraction implementation statistic approach 22 5 3 Shadow detection and removal In this section the implemented shadow detectors are discussed The focus is on moving shadows only so only pixels that are classified as foreground by the background subtraction
14. command line parameter This can be done by setting a value for the squared Mahalanobis distance c see section 5 5 If none of the K distributions match the new pixel value then the distribution with the lowest probability is replaced by a new distribution with a mean equal to the new pixel value The variance of this new distribution is set to a high value and its weight to a relative low value At each pixel X in a new frame the following update equations for the k distributions are performed 26 U a o Q E with E 1 for the matching model 0 for the remaining models 15 ais the learning rate or rate of adaptation This value ranging from 0 0 to 1 0 determines the importance of the previous observations Assigning high values to a results in the decrease of importance of these previous observations This parameter should also be given by the user as a command line parameter After this approximation the weights are renormalized The wand o parameters for unmatched distributions remain the same The parameters of a distribution that matches the new observation is updated as follows Ha 1 p u 4 PX 16 F5 1 PO A X 4y X 4 17 where p an X L 0 18 Now that there is a GMM for each pixel location a background model should be constructed from the GMM To accomplish this we look at those Gaussians in the mixture that are most likely produced by background processes These Gaussians can be found
15. contain enough classified foreground pixels are recorded If for example a frame was recorded which did not contain a foreground object at all we can conclude that a lot of misclassifications did occur in that frame using the background subtraction algorithm The steps executed for evaluating the two implemented algorithms using the video summarization component can be defined as follows 1 Create a frame level ground truth for each video extract the frame numbers manually in which one or more foreground objects vehicles bikers pedestrians etc are visible the tables in figure 15 32 2 Execute the video summarization algorithm described in figure 12 This results in a new video only containing those frames with enough classified foreground pixels The original frame numbers are also stored as an overlay on the frame itself 3 In this summarized video extract the successive intervals of frame numbers and store them tables of figure 16 4 Compare the found intervals with the frame intervals determined in the ground truth count the number of non overlapping frames The lower the number of these non overlapping frames the better the performance of the algorithm 6 4 Comparison results on practical video s For the three videos A B and C the evaluation steps described in section 6 3 are executed The three tables depicted in figure 15 contain the ground truth results for respectively video A B and C step 1 of the evaluation
16. gt library files e lt opencv directory gt lib e lt source code directory gt bloblib lib Finally use the libraries in the project itself by setting the library filenames in object library modules project gt settings gt link tab e cvblobslib lib CvBSLib lib cv lib cxcore lib highgui lib cvcam lib note CvBSLib lib is only required for the statistical background implementation The binaries themselves can be run using command line parameters When the program is executed using no parameters a list of parameters is shown To display the available cameras attached to the PC and their cam number type traffic exe cam list To do background subtraction with default additional parameters use one of the following set of required parameters e Do background subtraction using a live stream from a webcam traffic exe cam lt cam_number gt e Do background subtraction on a local video file avi on disk traffic exe mov lt movie_path gt e Do background subtraction on a local video file avi on disk and loop the movie traffic exe mov lt movie_path gt loop The deterministic background subtraction program also supports some additional parameters that can be set Place them after the required parameters e N lt value gt number of frames to create the initial background model default value 5 e T lt value gt subtraction threshold I x y B x y lt T default value 40 e I lt value gt initial backgrou
17. is the case with traffic queuing This also requires that the algorithm should cope with queuing vehicles that have speed zero These vehicles should clearly not be counted as background since they belong to the active traffic that we want to monitor Thirdly moving shadows should be detected and eliminated by the system This helps in the vehicle segmentation step since shadows can cause errors on density estimation and the unwanted merging of vehicles might occur Fourthly a number of traffic parameters should be extracted from the video streams By tracking the vehicles in the scene it is possible to calculate parameters like speed average traffic density queue lengths and times for each lane Fifthly The information from each stream in the system should be combined in such a way that a visual flow of traffic can be made in real time This flow can be recorded and stored in a cheap and efficient way The most important component in the system will be the background modelling system 6 Operations like tracking counting and segmentation of vehicles are not possible without a robust background system For this reason this Master thesis has its focus on this component 3 System overview In this section an overview of the components is given that should be present in a successful vision based urban traffic monitoring system Each component from this framework will be described in some detail here The framework should consist of o Multiple came
18. of unwanted foreground pixels is visible Figure 5 The creation of a unwanted foreground pixel _ k D d Figure 6 trail of unwanted foreground pixels as result of a car that passes by 17 By updating the background in the way described above the global lightning changes through time can be handled correctly To clarify and summarize this algorithm a flowchart of this algorithm is displayed in figure number 7 construct binary mask M x y fill with zeros I F create image buffer M of size N y ext video frame I x y available terminate algorithm store current frame in gt bailey Is buffer M of size N full construct initial background model is initial background using algorithm A2 or ode B x y created A3 DA foreground detection execute algorithm A1 page 10 t update bac ground model execute algorithm A4 Y display binary mask show forground objects Figure 7 flowchart of the background subtraction implementation deterministic approach Both the background model and mask are stored using the IplImage datatype available in OpenCV Operations on the images are efficiently executed when this datatype is used To identify the separate foreground objects a library cvBlobsLib is used to display bounding boxes around foreground objects The display of bounding boxes can also be disabled by the user with a command line parameter Website of the cvBlobs Li
19. statistical background subtractor As mentioned in 5 2 the statistical background implementation uses the cvBSlib This library has also a built in pixel based shadow detector which uses data from the underlying model of the pixel As mentioned before the basic idea of the shadow detector is that is classifies foreground pixels as shadow pixels when the pixel value has a significant lower value darker than it s corresponding background value derived in this case from the background model of that pixel 28 Also in the statistical approach the z parameter can be set by the user and has the same meaning as described in 5 3 1 The pseudo algorithm used for the shadow detector in the statistic implementation is as follows For each pixel that is initially classified as foreground pixel the following operations are executed to see if the pixel is a shadow pixel candidate e repeat for all used components in the background model of the pixel thresholded by parameter T in equation 19 o o o get the variance the weight and the mean values for R G and B let c R G B and UZ H H H calculate a ratio of similarity between the current pixel R G and B values and the means in the Gaussian component T Paa n 21 Z see appendix A for a graphical derivation of equation 21 When the current pixel is more intensive than it s mean S Will be greater than 1 if the current pixel is darker S will always be between 0 and 1
20. subtracted if there was no match from the weights of a Gaussian component The width of the band in o in which a sample will match a certain component is set by the threshold parameter c The higher the value of c the lower number of Gaussian components will be created As seen before in traffic situations multiple background components per pixel might be modelled for example in those locations where we have moving branches of trees Therefore the size of c should not be too big but also not too small For all the videos in this research we use a value of 64 for the c parameter Since the shadow detection algorithm is basically the same for both implementations the same values for the S parameter in this statistic setting are used as those in the deterministic setting 6 3 Comparison measure The most ideal way to test the performance of the implemented algorithms is to first create binary masks manually in which the foreground pixels are highlighted by the user the ground truth These binary masks can then be compared with the binary masks created by the background subtraction algorithms Videos A B and C altogether contain thousands of image frames Creating the ground truths manually is therefore too time consuming Using the Video Summarization component as evaluation tool In this research another evaluation measure is chosen The video summarization component defined in 5 4 is actually used as an evaluation tool Only frames that
21. 0 pp913 929 Sept 2003 41 28 Z Zivkovic Improved Adaptive Gaussian Mixture Model for Background Subtraction In Int Conference Pattern Recognition Vol 2 pages 28 31 2004 29 Website for this research including digital version of this paper sample videos test results and the binaries of the implementations http www science uva nl msmids afstuderen master 42 Appendix A Graphical derivation of the Spcp formula let c R G B and w 4l Hg Lp the implemented ratio of similarity between the current pixel R G and B values and their calculated means is eu _ Rup Gug Bu led Hp Hg My RGB The ratio can be graphically shown as being the ratio between ed and H After normalization and using the in product this ratio becomes a Wal ey lel a RGB Appendix B Rewritten formula for colour distortion let c R G B and E Mes Hy A check with respect to colour distortion is done by first calculating the square distance vector between vector c and S pE D Sop E0 3S rorta RV O rotte OF S pasta BY Again no square root operation is present in the right term which optimizes the execution of the program Distances D green and M mo Sic red can be visualized like this 43 Note that the position of vector M depends on the value of Speg For lower values of Speg the vector M will be moved down perpendicular to E for example M 44
22. 5 4 Video SUMIMARIZAGION L A mam ANAIRYN NA RANBIYG ANAN TGA Kini 25 5 5 How to use the SOFTWAPE cc csscscssssssssscssssesesscssscesssscsessesecscsesssescassessesesaesesecseees 26 AA ation a iia Hane anaes ait RRS 27 6 1 Types Of AA AT 30 oe nG Da LS AA AA AT 30 6 3 Comparison measure nasaan 32 6 4 Comparison results on practical video s X aa 33 7 CONCIUSIONS nanan i e ara iets setae cask oe tang pea NA FR PRR GAAN BAR OES 37 GB FETUS WO NG areola E LI 38 e RETETEM NAA AT 40 Appendix A Graphical derivation of the Seep formula aa 43 Appendix B Rewritten formula for colour distortion 2XXXX a 43 1 Introduction Monitoring traffic using a vision oriented system in crowded urban areas is a challenging task To have an overview of the traffic flow through the city can be very advantageous when new roads in the city are built or reconstructed Also traffic light wait times can be programmed more efficiently when long term traffic flow through the city is known Current approaches of monitoring traffic includes manual counting of vehicles or counting vehicles using magnetic loops on the road The main drawback of these approaches besides the fact that they are expensive is that these systems only count Vehicle classification speed and location registration for individual vehicles and vehicle size are properties that are not or only limited suppor
23. 64 1409 2058 2508 798 2148 2172 319 1453 1454 799 836 2249 2536 368 1588 1589 946 948 422 423 1734 1776 482 1867 1868 536 537 2123 2176 615 658 2249 2502 712 713 2805 780 781 Figure 16c results of the deterministic and statistical background subtraction algorithms applied on video C together with the ground truth intervals Value of parameter Rb 250 As can be seen from the table above miscalculations do occur using the two implemented subtraction methods To get a better view of the frame intervals of the ground truths and the frame interval results of both algorithms a graph is plotted for each video in figure 17 In this graph the frame numbers are located on the X axes and corresponding bins mark those frame numbers that were recorded green and red bars The yellow bars mark the ground truth intervals in these intervals one or more objects were present in the scene To obtain a score for both background subtraction algorithms the number of frames in which miscalculations did occur E are counted and compared to the total number of frames N of the videos S a x100 24 In this way S is the percentage of frames that were recorded correctly For videos A B and C these scores are calculated for the deterministic and statistical subtraction method This corresponds with the last evaluation step as was stated in section The results are displayed in figure 17 Score S deterministic Score S statistical total approach
24. Background Subtraction for Urban Traffic Monitoring using Webcams Master Thesis By Mark Smids msmids science uva nl Supervised by Rein van den Boomgaard rein science uva nl University Universiteit van Amsterdam FNWI Date December 2006 Abstract This research proposes a framework of a visual urban traffic monitoring system capable of obtaining the traffic flow at intersections of a city in a cheap way The main focus of this research is on the background subtraction and shadow elimination component of this proposed system Two different background subtraction techniques are implemented and compared a deterministic approach using an adaptive background model and an statistical approach using a per pixel Gaussian mixture model GMM to obtain information about the background Furthermore as a way of testing the two background subtraction algorithms a smart surveillance system is built using a video summarization technique which only stores those parts of the scene that include traffic The system is evaluated using traffic scenes recorded under different weather conditions Subtraction results of the deterministic subtraction approach show some important limitations The total area of an foreground object is often not fully detected and objects in the scene that have become part of the background are wrongly classified as foreground objects for a long period of time Results based on the GMM subtraction approach are very promisi
25. ally crass validate between different models to improve performance 14 A drawback of this multiple background modelling and the cross validation is that it is very computational expensive Furthermore in an urban setting objects move at a very different speeds which results a large number of different background models Finally to come up with a solution for the third drawback is very important since especially in urban traffic monitoring we are interested in vehicle classification and road density what percentage of the road is occupied on average To obtain representative measurements for these features it is necessary to remove all shadows from moving candidate objects in the image frames In the next section shadow elimination will discussed 12 4 5 Shadow detection and elimination The goal of a successful shadow elimination algorithm is to prevent moving shadows being misclassified as moving objects or parts of them If shadows are not eliminated it could happen that two separate objects will be merged together by the system when the shadow of one object is overlapping with the second object A shadow can be classified as a self shadow or a cast shadow A self shadow is the part of the object which is not illuminated directly by the light source In our case of traffic monitoring the relevant light sources are the sun street lights and vehicle lights The cast shadow of an object is the area projected on the scene by the object
26. ames default value 0 e B lt value gt show bounding boxes around foreground objects 0 no 1 yes default value 0 e R Rb lt value gt Record those frames where the number of classified pure foreground pixels exceeds lt value gt A window will popup where the user can select the codec Use Rb for pre and post recording default no recording e clean show the incoming stream without any operations performed This can be used to show the original speed fps of the stream The result of the algorithm on each video frame is shown in a window The program closes automatically as soon as the stream of incoming frames ends or when the user closes the application Also for easier use of the software a combined GUI figure 11 for both background subtraction methods has been created in which the parameters can be set in a more intuitive way Parameters T C S of the statistical algorithm and parameters N T I S of the deterministic algorithm can be adjusted in a window that will appear when the Additional Parameters button is pressed The more basic parameters like the choice of the algorithm and the output mode O can be accessed directly from the main panel Also the video summarization component can be activated parameter R from this main panel which enables recording only frames in which enough foreground pixels are present as was described in section 5 4 A configuration of parameters can be stored to disk using the
27. aplA O26 07541 DES OFLEL 05601 0928 0299 OS8eP OGLE uopsegang punoiby2eg ansjulwwajag ay Bujsn s ewajul awe papiosay yoeoldde uoqseqqns puncibyseg jeaysners ay Husn sjewaju awe papiooay BIGISH 81am Sa qo punoubalo 810W 40 BUO alay s emajul aWe14 UUL punog Y DARIA OLLRE 0 691Z 08261 02894 OOFFL OS0z 0596 DEZ Ozer UN YA Figure 19 graphs displaying recorded frame intervals using the two background subtraction methods The yellow bars mark the intervals where one or more objects are present in the scene 9 References 1 2 3 4 5 6 7 8 9 10 11 12 13 S Gupte O Masoud R F K Martin N P Papanikolopoulos Detection and Classification of Vehicles IEEE Transactions On Intelligent Transportation Systems Vol 3 No 1 March 2002 R Cucchiara M Piccardi Vehicle Detection under Day and Night Illumination Proceedings of 3 rd International ICSC Symposium on Intelligent Industrial Automation IIA 1999 M Betke E Haritaoglu L S Davis Multiple Vehicle Detection and Tracking in Hard Real Time IEEE Intelligent Vehicles Symposium pp 351 356 July 1996 D M Ha J M Lee Y D Kim Neural edge based vehicle detecting and traffic parameter extraction Image and Vision Computing Vol 22 2004 pp 899 907 A J Lipton H Fujiyoshi R S Patil Moving Target Classification and Tracking from Real time Video In 4th IEEE Workshop o
28. arch Engines Survey on color for image retrieval from Multimedia Search ed M Lew Springer Verlag January 2001 T Gevers and A W M Smeulders Color Based Object Recognition In Proc of Pattern Recognition 32 453 464 1999 R Cucchiara M Piccardi and A Prati Detecting moving objects ghosts and shadows in video streams IEEE Transactions on Pattern Analysis and Machine Intelligence 25 pp 1337 1342 Oct 2003 L M Fuentes S A Velastin From tracking to advanced surveillance In Proc 2003 International Conference on Image Processing Volume 3 Issue 14 17 Sept 2003 Page s III 121 4 vol 2 A W K So K Y K Wong R H Y Chung F Y L Chin Shadow Detection For Vehicles By Locating The Object Shadow Boundary In Proc IASTED Conference on Signal and Image Processing SIP 2005 pp 315 319 2005 F Porikli J Thornton Shadow Flow A Recursive Method to Learn Moving Cast Shadows IEEE International Conference on Computer Vision ICCV ISSN 1550 5499 Vol 1 pp 891 898 October 2005 C Stauffer W E L Grimson Adaptive background mixture models for real time tracking In Proceedings IEEE Conf on Computer Vision and Pattern Recognition pp 246 252 1999 P Kaew Tra Kul Pong R Bowden A Real time adaptive visual surveillance system for tracking low resolution colour targets in dynamically changing scenes In Journal of Image and Vision Computing Vol 21 Issue 1
29. asured over time So the first step is to model the recent history X X by a mixture of K Gaussian distributions The probability of observing the current pixel value can be written as follows 26 k P X 0 MX Mj Zig 13 i l Where K is the number of Gaussian distributions is the weight of the i th Gaussian at time t and 3 are the mean and covariance matrix of the i th Gaussian in the mixture at time t For computational reasons 2 is of the form a I which assumes independence of the colour channels and also the same variance for each channel y is a standard Gaussian distribution function of the form 19 1 LX m EX py MA Ho Z a e 1 27 2 2 14 The value for K the maximum number of Gaussian components depends on the amount of memory present In the implementation K is set to a fixed value of 4 n is the dimension of the data In case of grey value images this value is 1 if the image is in colour n should be 3 A new incoming pixel will most likely be represented by one of the major components of the mixture model If this is the case then the corresponding component in the mixture is updated using that new pixel value So every new pixel X is matched against the K Gaussian components until a match is found A match can be defined as a pixel value that is within 2 5 standard deviations of a distribution 26 In our implementation the width of the band can be set by the user using a
30. bject is moving through shadowed areas the deterministic background subtraction approach is often only able to detect parts of the object Because of the dark regions in that shadow area colours in that area tend to be more similar As described in section 5 1 and figure 6 trails of unwanted foreground pixels might occur using the deterministic subtraction algorithm Despite of the solution proposed in the corresponding section still a number of those unwanted pixels are visible in the videos containing the subtraction results An extension to the solution is that the entire background model will be refreshed as soon as the number of foreground pixels do not change much in an interval of a certain number of successive frames In the experiments performed the background model is totally refreshed when during five successive frames the number of classified foreground pixels differs no more then 5 pixels As said before creating a ground truth for each frame by marking all pixels belonging to a foreground object is very time consuming The results of such an evaluation are very interesting and definitely worth analysing which results in a pixel level evaluation of both background subtraction algorithms An evaluation on this level is not included in this research and is proposed as future work 7 Conclusions In this research two different background subtraction techniques for traffic monitoring were implemented and compared The deterministic s
31. brary http opencvlibrary sourceforge net cvBlobsLib 18 5 2 Statistical background model mixture of Gaussians The second implemented background subtraction system is based on a statistical approach proposed by 26 and elaborated further by 28 For each pixel in the scene a Gaussian Mixture Model GMM is considered At any time t what is known about a particular pixel x0 y0 is its history X X t x y i 1sist where 1 is the image sequence When making scatter plots of these history values it becomes clear that a GMM is needed to construct an accurate background model In figure 8 a R G scatter plot is showing a bi model distribution of a pixel s value over time resulting from CRT monitor flickering 26 It can be seen that two clusters of points are formed This is already an indication that a single model Gaussian is not sufficient to create a reliable background model Another interesting aspect is what to do with the pixel value history For example the value of a certain pixel in a scene might change abruptly for a long time when it becomes cloudy at daytime As a result multiple clusters will also occur in the long term scatter plot So it can be concluded that not all the pixel values in the history should be used and more recent observations should get a higher weight 200 180 160 pali x 1 1 1 1 0 50 100 150 200 25 R a o 300 Figure 8 R G Scatter plot of one particular pixel me
32. component could be candidates for shadow pixels As mentioned in chapter 4 5 detecting moving shadows is very important for reliable segmentation and vehicle classification figure 3 Both background subtraction algorithms use an almost similar shadow detection algorithm In the statistical setting an algorithm is applied that reuses data of the Gaussian components to predict which foreground pixels are actually shadow pixels This algorithm will be described in further detail in 5 3 2 For the deterministic approach a shadow detector is implemented which is based on statistics gathered from pixel values of a number of history frames see 5 3 1 The similarity between the two algorithms lies in the fact that both detectors classify a foreground pixel as shadow pixel when the pixel value has a significant lower value than it s corresponding background value 5 3 1 Shadow detector for the deterministic background subtractor In the implementation of the deterministic approach no statistical information is stored about the history of the pixels For the correct working of the shadow detection algorithm a history of values for each pixel in previous frames is needed to derive the mean of the history values The number of history frames to consider can be adjusted by the user as a command line parameter sf We use the property stated in 28 that a pixel is classified as a shadow pixel when the pixel value has a significant lower value than it s corres
33. count both R G and B channels of the video frame o Linear predictive filter In this method the current background estimate B x y is computed by applying a linear predictive filter on the pixels in the buffer of size n A predictive filter uses information of history frames to predict the values in future frames In the setting of a background modelling algorithm a linear predictive filter can be written as follows B x y al x y 8 i l wherea is the predictive coefficient at frame i in the buffer A disadvantage of this method is that coefficient based on the sample covariances needs to be estimated for each incoming frame which makes this method not suitable for real time operation o Non parametric model This method discriminates itself from the mentioned methods above by not using a single background estimate B x y at each pixel location but using all the frames in the buffer to estimate a non parametric estimate of a pixel density function see 14 A Gaussian kernel estimator is usually used to model this background distribution A pixel can be declared as foreground if it is unlikely to come from the formed distribution The advantage of using non parametric models for estimation is that it can correctly identify environmental movements like moving tree branches which are undesired foreground objects Recursive background modelling techniques do not keep track of a buffer containing a number of history frames
34. d hue component and its goal is similar as described in chapter 4 3 The statistic approaches can also be divided further In the statistical methods the parameter selection is critical issue so we have parametric and non parametric statistical approaches An example of the latter approach is where colour is considered as a product of irradiance and reflectance 11 The distortion of the brightness and chrominance of the difference between the expected colour of a pixel and its value in the current image are computed The rationale used is that shadows have similar chromaticity but lower brightness than the background model A statistical learning method might be used to automatically determine the thresholds for the components of the expected colour 11 Finally in the parametric statistical approach often two sources of information are used to help detecting shadows and objects local information individual pixel values and spatial information objects and shadows are compact regions in the scene For example the Anton system 10 13 uses the local information in the way that if v R G B is the value of the pixel not shadowed a shadow changes the colour appearance by means of an approximated linear transformation 048 0 O v 0 047 0 v Dv 12 0 0 051 The authors also note that in traffic video scenes the shadows often appear more bluer Given the means and variances for the three colour channels for reference points the shadowed versio
35. d objects of a certain size should never be incorporated in the background model Another improvement might be found in the choice of another colour model in the pre processing step Transforming the RGB space to normalized RGB or the HSV colour model might result in better subtraction results especially when illumination changes often occur Finally the evaluation of the implemented algorithms in this research could be extended by evaluating them on scenes for which manually annotated ground truth frames are available The results of such an evaluation could be very interesting and is definitely worth analysing which results in a pixel level evaluation of both background subtraction algorithms 38 OS0LZ O9FSC 06902 06061 UOIIeNgNS punoibyseg ansjuwlajag ay Guisn sjewajul awed paplozay yoeoidde uoyseqgns punoibyaeg je2nsne s ay Guisn sjewajul awey papiosay alq s 8am spalgo punoibalo aio Jo BuO aJaym s emajul awe YIN punog 2 08pln UFZOE ng DE291F OL8S OFgse y DERBE O 6EEP s OpZ 0 860F 8 OSOSE 0458k 0 98ZE OOLGE 02908 OSLEE T Osrsc OFELE o 0 E682 00541 O L691 OTEPL DELZ OFLLL OFS6 0564 Og9e9 O22F Osle 06S a o v ng ng v ng o o a a uonagaang punoibyagkg INSIUIWUA Aag ay Hulsn s galaju awel papioday yseodde voqseujgns punopyseg jeaysyes ay Husn seau awe papingay BIGISIA a13 Sago punoIbaJo alow Jo auo aJaym S eAJa ul awel UNL punog 9 0
36. e wand o are the mean and the standard deviation of IT x Y B x y for all neighbouring pixels around x y Although in general better results are obtained this step is computational more expensive since it requires an extra 11 scan through both the current image frame and the current background model to obtain the mentioned mean and standard deviation Instead of T being a scalar it would be ideal to let T be a function of the spatial location x y For example in regions of low contrast the threshold should be lower since pixel values of the background and relevant foreground objects are more close together in that case Another approach is to use the relative difference rather than absolute difference to emphasize the contrast in dark areas such as shadows 23 In that case the equation will be Z x y B x y gt B x y T 10 A drawback of this method is that it cannot be used to enhance contrast in bright images such as an outdoor scene under heavy fog There are also approaches where two thresholds are used 22 The basic idea is to first identify strong foreground pixels whose absolute differences with the background estimates exceeded a large threshold Then foreground regions are grown from strong foreground pixels by including neighbouring pixels with absolute differences larger than a smaller threshold This process is called hysteresis thresholding 4 4 Data Validation The goal in this step of
37. eground Since one of the implementations in this research will use a MoG model this method is described in more detail in chapter 5 2 4 3 Foreground detection The goal of the foreground detection component is to obtain a binary mask image M x y for each video frame In this mask only objects that belong to the foreground will be visible Basically in this step it should be determined which pixels in the current image frame are candidates of being foreground pixels The images required for this component are the current background model 8B x y obtained in the component described in 4 2 and the current incoming image frame from the camera x y after pre processing The most commonly used approach for foreground detection is to check whether the input pixel is significant different from the corresponding background estimate The creation of a binary mask M x y for a greyscale video frame with height h and width w can be described by for x 1 tow for y 1 to h if Z x y B x y gt T then M x y 1 else M x y O A1 endfor endfor where T is a threshold value In most cases the value for T is determined experimentally and therefore depends strongly on the setting of the scene Another popular foreground detection scheme is to threshold based on the normalized statistics In that case Z Y B x y bp T in the pseudo algorithm above should be replaced with LE Y B BY Hr 9 ox which is called the z score wher
38. etecting Moving Shadows Algorithms and Evaluation IEEE Transaction On Pattern Analysis and Machine Intelligence Vol 25 No 7 July 2003 40 14 15 16 17 18 19 20 21 22 23 24 25 26 27 A Elgammal D Harwood L Davis Non parametric Model for Background Subtraction In Proc of the 6th European Conference on Computer Vision Part II Pages 751 767 2000 S C S Cheung C Kamath Robust Background Subtraction With Foreground Validation For Urban Traffic Video In Journal Eurasip Journal on Applied Signal Processing Journal Volume 14 2004 R T Collins A J Lipton T A System for Video Surveillance and Monitoring tech report CMU RI TR 00 12 Robotics Institute Carnegie Mellon University May 2000 S C S Cheung C Kamath Robust Techniques For Background Subtraction In Urban Traffic Video In Proceedings of the SPIE Volume 5308 pp 881 892 2004 D Beymer P McLauchlan B Coifman J Malik A Real time Computer Vision System for Measuring Traffic Parameters In Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1997 M B van Leeuwen F C A Groen A Platform for Robust Real time Vehicle Tracking Using Decomposed Templates Intelligent Autonomous Systems Group Faculty of Science University of Amsterdam Amsterdam The Netherlands unpublished 2001 T Gevers Color in Image Se
39. for endfor When the initial background model is created the background subtraction process starts For each new frame J x y that comes in it is first subtracted from the current background model using the first pseudo algorithm given in chapter 4 3 A1 adjusted for colour instead of greyscale frames The value for the threshold parameter T should also be given by the user as a command line parameter This subtracting results in a binary mask where foreground objects are visible in white and the background in black The next step in the process is to update the current background Only all the pixels that are classified as background in the mask M x y are updated in the background model B x y in the way described in the pseudo algorithm A4 for x 1 tow for y 1 to h if M x y 0 and I x y R B x y R ka and I x y G B x y G ka and x y B B x y B ka then A4 B x y 50 B x y 1 x y endfor endfor where T 10 which is determined experimentally The concatenation of I x v B x y Ka conditions is needed to avoid the creation of clusters of foreground pixels which are in reality just background Figure number 5 demonstrates how these clusters are formed when the mentioned conditions are not used Assume that the black 16 area background has pixel values of 0 The white area object of interest has pixel values of 70 and the grey area object shadow and or noise artefacts has values of 35 Now also a
40. forms infinite dimensional spectral colour space to a three dimensional RGB colour space via red green and blue colour filters 11 As a result of this linearity and sensor type the following characteristics of the output images hold o Variation in colour It rarely occurs that we observe the same RGB colour value for a given pixel over a period of time o Band unbalancing Cameras typically have different sensitivities to different colours A possible approach taken is to balance the weights on the three colour bands by performing a normalization 11 The pixel colour is normalized by its standard deviation s which is given by AA KOKO KUA 1 where Or 1 Og i and Op i1 are the standard deviation of the it pixel s red green and blue values o Clipping The CCD sensors have limited dynamic range of responsiveness All visible colours lie inside the RGB cube spanned by the R G and B vectors with a given range As a result some pixel values outside the range are clipped in order to lie entirely inside the cube To reduce this unusual shape of the colour distributions s is often set to a minimal value if a pixel s s is zero Also in the pre processing step it can be advantageous to transform from the original RGB colour space to an invariant colour space One such system is the normalized RGB colour space defined as R G B 2S p 2 R B G R B G R B G where R G B are the values for red green and blue coming directly from t
41. gories will be introduced shortly The methods described next all assume a highly adaptive modelling which means that the model is updated using only a fixed sma number of history frames In this way current incoming frames have a great influence on the final model it can adapt quickly In non recursive background modelling techniques 2 3 5 14 22 a sliding window approach is used for background estimation A fixed number of frames is used and stored in a buffer With a sliding window size n the background model B x y at a certain time t is given by B x y FLAP nI 5 where Z x y is the image frame coming from the camera at time t and F is a function that is based on the temporal variation of pixel values and can be obtained in different ways as given in literature o Frame differencing This is the most easy and intuitive background modelling method It uses the video frame at time t 1 as the background model for the frame at time t The background model at time t can be summarized as B x y L y 6 o Median filter This is one of the most commonly used background modelling techniques In a greyscale video stream the background is estimated using the median at each pixel location of all the video frames in the buffer of length n B x y median 1 x Y T XY 7 Median filtering can also be applied on coloured video frames Instead of taking the median 22 uses the mediod which takes into ac
42. he camera and r g b are the resulting normalized RGB colour components This colour space has the important property that they are not sensitive to surface orientation illumination direction and illumination intensity 20 A disadvantage is that normalized colours become unstable when the intensity is small A more intuitive colour model is the HSI colour system which defines a value of hue saturation and intensity for each pixel value The values H S and I are obtained from the R G and B values in the following way 20 ___v3 G 8 ite jee 3 R G R B 3 By taking a linear combination of H and S values an invariant model can be intuitively constructed Another popular invariant colour model is the c c2c model see 12 and 21 This model is proven to be invariant to a change in viewing direction object geometry and illumination The features are divined as C TT E C P E G mae 4 max G B max R B max R G 4 2 Background Modelling In the background modelling stage not only a model of the background of the scene should be constructed once but also the model should be adaptive The general aim of the background modelling component is therefore to construct a model that is robust against environmental changes in the background but sensitive enough to identify all relevant moving objects Background modelling techniques can be classified into two broad categories 17 non recursive and recursive these cate
43. hms are then finally evaluated by testing them on a number of videos of traffic flow at crossroads To test the system in real time and for a long period of time monitoring using a webcam is also possible with the created software The software can also function as a smart surveillance system where only the scenes of interest are recorded to disk using video summarization techniques The video test samples project information this thesis in digital format and the software itself can be obtained from a website 29 This Master thesis will now continue in the following way in chapter 2 the requirements of a robust urban traffic monitor system are discussed Also the setting and properties of a typical crossroad in an urban area is given here Chapter 3 gives an overview of the proposed system and defines its components Existing literature and methods on background subtraction and shadow elimination will be discussed in chapter 4 Chapter 5 includes the description of the two implemented background subtraction algorithms and a short user manual for the software Finally in chapter 6 the two implemented approaches are evaluated and compared The goal of this research is to give an answer to the question which approach a deterministic or statistical approach performs better background subtraction results in the given setting 2 System Requirements and setting 2 1 The Setting In this section the environmental setting of this research wil
44. i video source is a wabcam Store current frame and number of foreground pixels in the buffer queue the number of foreground pixels in the current frame M gt recording threshold 10 previous frame was not recorded and the videosum buffer is full write to disk those frames in the buffer recording threshold 10 empty the videosum buffer Figure 12 Flowchart showing the extended video summarizing algorithm 29 6 1 Types of videos As stated in the introduction this research has its focus on traffic monitoring in an urban setting Typical interesting traffic points in cities are crossroads Three videos are recorded looking down on a typical small crossroad in an urban area In each of the videos the weather conditions are different sunny clouded and rainy The camera that has been used is a Philips ToUcam Pro webcam with a capture resolution of 640x480 and its frame rate was set to 15 fps Still frames of the videos are displayed in figure 13 A Weather conditions clouded very windy Length 5 minutes 4581 frames Traffic lights no Colour yes Resolution 640x480 Special also bikers and pedestrians B Weather conditions sunny Length 4 5 minutes 4581 frames Traffic lights no Colour yes Resolution 640x480 Special also bikers and pedestrians c Weather conditions rainy Length 3 5 minutes 3024 frames Traffic lights no Colour yes Resolution 640x480
45. l be discussed and given this setting the requirements of a traffic monitor system will be given Getting a complete overview of requirements and possible difficulties that might occur is an important first step in the design of such a complex system The first assumption is that the focus is on the monitoring of urban traffic and monitoring the flow of traffic through a city Typical places to mount camera s will logically be at important intersections and crossroads By identifying vehicles and keep track of their direction in each stream an overview of the flow of the traffic can be realized in a cheap and efficient way Now an overview of characteristics that will be present in general video streams from an urban intersection scene is given o The roads leading to the intersections can have multiple lanes o Vehicles and pedestrians enter and leave the scene This does not necessarily happen at the border of the image frame for example a pedestrian can enter the scene by coming out of a building standing nearby the intersection o Queues of vehicles will often appear since traffic signs may be present As a result vehicles will drive at very different speeds o Cast shadows and self shadows of the vehicles and other objects might appear in the scene Furthermore the shadows can disappear and reappear in a short period of time if the weather is cloudy o Also as a result of cloudy weather sudden global illumination changes might occur often
46. lly in the interval 0 636 The deterministic algorithm has more difficulties with rainy situations In the interval 0 636 it can be seen that a lot of unwanted frames have been recorded In this part of the movie the rain is most intense so it can be concluded that the statistical approach is more robust in rainy scenes It is also interesting to look at the different kinds of misclassifications A misclassification can be either the frame that was recorded but actually should not be recorded since no foreground objects were visible Ee or the frame was not recorded but the frame actually does contain a foreground object and therefore should be recorded Enot rec In the application of a surveillance monitor the latter misclassification is more severe than the former since it is better to record some unnecessary frames than not recording frames containing possible interesting objects The ratios between those two types of misclassifications in the test videos are displayed in figure 18 below Mis Deterministic approach statistical approach Classifications E Enot rec E EET Video A 0 64 0 36 431 247 0 53 0 47 283 250 Video B 0 89 0 91 42 432 0 55 0 45 123 100 Video C 0 66 0 34 328 170 0 25 0 75 51 150 Figure 18 ratio between the two different kinds of misclassifications for each of the test videos From figure 18 it can be seen that for video A the misclassification ratio of the deterministic appr
47. n dog and cyclist 450 Figure 15c ground truth results for video C which has a total of 3024 frames and 735 frames in which foreground objects were visible For videos A B and C the results of the deterministic and statistical background subtraction algorithms together with the ground truths are displayed in the tables in figure 16 results of step 3 of the evaluation In the table for video A it should be noted that the value for parameter Rb was set to a high value 1200 due to relatively a high number of misclassifications caused by the swinging tree branches in the wind it will take a number of frames before the implemented Gaussian Mixture model can handle the swinging tree branches problem Recorded frame intervals using the statistical Frame number intervals in which foreground objects are Recorded frame intervals using the deterministic approach visible ground truth approach 421 463 280 298 2447 2526 279 479 647 690 310 325 2606 2616 647 903 777 864 339 344 2635 2670 1059 1103 1023 1107 367 382 2901 2920 1467 1576 1464 1579 401 410 3196 3204 2067 2109 2066 2110 416 450 3460 3499 2214 2263 2213 2264 455 462 3639 3845 2442 2538 2441 2541 644 915 4018 4039 2634 2671 2634 2672 1052 1104 4188 4217 3641 3682 3563 3890 1467 1558 4226 4244 3692 3767 4190 4233 2074 2099 4282 4296 3793 3845 4316 4386 2103 2110 4327 4383 4190 4218 4485 4513 2210 2283 4484 4500 4226 4232 2315 2330 4510 4514 4329 4384 2362 2413 4485 4499
48. n Applications of Computer Vision pp 8 15 1998 Z Zhu G Xu Bo Yang D Shi X Lin VISATRAM a real time vision system for automatic traffic monitoring Image and vision computing 0262 8856 Zhu vol 18 pg 781 2000 M Kilger A shadow handler in a video based real time traffic monitoring system In Proc IEEE workshop on Applications of Comp Vision 1992 pp 11 18 P L Rosin T Ellis Image difference threshold strategies and shadow detection In Proc of the sixth British Machine Vision Conference 1994 P Kaew Tra Kul Pong R Bowden An Improved Adaptive Background Mixture Model for Real time Tracking with Shadow Detection In Proc 2nd European Workshop on Advanced Video Based Surveillance Systems AVBS 01 Sept 2001 A Prati I Mikic c Grana M M Trivedi Shadow Detection Algorithms for Traffic Flow Analysis a Comparative Study In Proc IEEE Intelligent Transportation Systems 2001 T Horprasert D Harwood L S Davis A Statistical Approach for Real time Robust Background Subtraction and Shadow Detection Proc IEEE ICCV 99 FRAME RATE Workshop Kerkyra Greece September 1999 E Salvador A Cavallaro T Ebrahimi Shadow Identification and Classification using invariant Color Models In IEEE Signal Processing Soc Int l Conf Acoustics Speech and Signal Processing ICASSP 2001 IEEE Press 2001 pp 1545 1548 A Prati I Mikic M M Trivedi R Cucchiara D
49. nd model 0 use mean 1 use median see A2 and A3 default value 1 e S lt value gt value for T the threshold for shadow darkness as described in chapter 5 3 1 default value 0 5 e Sf lt value gt size of the history buffer used for calculating statistics needed in the shadow detection algorithm e O lt value gt output 0 binary mask only 1 mask and current frames default value 0 e B lt value gt show bounding boxes around foreground objects 0 no 1 yes default value 0 e R Rb lt value gt Record those frames where the number of classified pure foreground pixels exceeds lt value gt A window will popup where the user can select the codec Use Rb for pre and post recording default no recording e clean show the incoming stream without any operations performed This can be used to show the original speed fps of the stream 26 The statistical background subtraction program also supports some additional parameters that can be set Place them after the required parameters e T lt value gt size of the frame buffer number of history elements Alpha 1 T default value 1000 e C lt value gt threshold on the squared Mahalanobis distance to decide if it is well described by the background model or not default value 64 e S lt value gt value for T the threshold for shadow darkness as described in 5 3 2 default value 0 5 e O lt value gt output 0 binary mask only 1 mask and current fr
50. ng The limitations mentioned above do not apply for this method and is therefore far more effective in the urban traffic setting Keywords background subtraction video summarization shadow detection Gaussian mixture model OpenCV smart surveillance x bY x FACULTEIT DER NATUURWETENSCHAPPEN WISKUNDE EN INFORMATICA Table of Contents T Wat gee KC AA AGA ee 3 2 System Requirements and SettiING ccc ccessssscsseesssscsceseescsssesssescsssessesscssseeeesesseescees 4 PA AA PA AA 4 2 2 The tasks and requirements of a robust urban traffic monitor system 4 3 System OVEFVICW u s 5 4 Literature on background Subtraction algorithms 111 7 aa 6 a AA AA AA AA AA 6 4 2 Background MEO GING aaa mna KAG NLA ahahah kng Kon kaka nA Ga 9 4 3 Foreground detection aaa RANA a eles NA ABA kk patna en Lipa 11 aE Data re 2 2 8 ee ee ee ee ab AG kaa T 12 4 5 Shadow detection and eliMination nn ccc ccsscsesecscsseseeecsesecseessseseeseeessesees 13 5 Aa AA 15 5 1 Deterministic background modeL X XX ana nanananamana 15 5 2 Statistical background model mixture of Gaussians a 19 5 3 Shadow detection and removal c ccc ccccssssssecscsseseeecscsseseescsesecsecscsesseseeecseessesaeas 23 5 3 1 Shadow detector for the deterministic background subtractor 23 5 3 2 Shadow detector for the statistical background subtractor 24
51. ns of means and variances will then become 4i uid and cip ojd with ie R G B The spatial information is extracted by performing an iterative probabilistic relaxation by using information of neighbourhood pixel values A drawback of parametric approaches is that the parameters need to be selected very carefully 14 Manual segmentation of an initial number of frames is often needed to obtain good learning results An expectation maximization EM algorithm can be used to automatically find these parameter values while developing and evaluating a shadow detector three important quality measures are very important to consider 10 First of all good detection is needed low probability of classifying shadow points as non shadow points Secondly the probability to identify wrong points as shadow should be low good discrimination and finally the points marked as shadows should be as near as possible to the real position of the shadow point good localization Prati et al performed an evaluation of the general types of shadow elimination described in the previous paragraph 10 13 They came up with some important conclusions When a general purpose shadow detector is needed with minimal assumptions a pixel based deterministic non model based approach assures best results To detect shadows in one specific environment more assumptions yield better results and the deterministic model based approach should be used If the number of object classe
52. oach is better than in the statistical approach smaller Enot rec although the total score S in figure 17 is slightly better when using the statistical approach For video B it can be seen that the misclassification ratio of the deterministic approach is very bad Almost all misclassifications were frames that should be recorded but were not This is mainly caused by the inability of the deterministic approach to detect objects in shaded areas Finally figure 18 shows for video C that the ratio of misclassifications in the statistical approach seems disappointing but it should be noted that the number of Erec was very low in this case Comparing the absolute values between both approaches 150 170 shows a somewhat equal number of misclassifications of this type The video summarizations of video s A B and C can be downloaded from the project website 29 Also the background subtraction result foreground pixels highlighted for each frame of the three videos can be accessed from the website In these video the performance of the shadow detector is also visible The foreground pixels that are highlighted in green are actually classified shadow pixels In general we see here better results for the statistical background subtraction algorithm in terms of detecting the whole object of interest In the deterministic approach holes will often appear in the 36 foreground objects Furthermore in the sunny scene video B it should be noted that when an o
53. penCV 5 1 Deterministic background model The algorithm described in this section is based on the methods defined in 1 and 4 As a prerequisite for this algorithm N frames should be available which represents a perfect background of the static scene So in our setting we want N frames of the intersection when no traffic is present on the roads The first step of the algorithm is to take a pixel wise average or median of the N frames that will result in the initial background model If the video frames have a resolution of w x h then the initial background model B x y c with c e R G B calculated from mean values will be Website of the Open Source Computer Vision library http www intel com technology computing opencv 15 for x 1 to w for y 1 to h for c 1 to 3 1 B x y c ee ae YD A2 endfor endfor endfor As said before it is also possible to take the median of the N frames as the initial background model This can be specified by the user by changing a command line parameter see 5 5 The advantage of taking the median is that an outlier pixel within the N frames is filtered out and does not contribute in the final initial background model In case the median is used N should be odd and the background model B x y will be for x 1 tow for y 1 to h for c 1 to 3 vector v Il x y c L x y c Iy x y c A3 order pixel values in v from low to high B x y c v N 1 2 endfor end
54. ponding background value derived from the history frames Also a parameter can be set by the user 7 which is a threshold on how much darker the shadow can be For example 7 0 5 means that if the pixel is more than two times darker then it is not shadow The pseudo algorithm used for the shadow detector in the deterministic implementation is as follows e for the first Sf incoming frames store the pixel values of these frames in a buffer F shadow detection will be active after these Sf frames are stored e for each pixel that is initially classified as foreground pixel the following operations are executed to see if the pixel is a shadow pixel candidate o calculate the R G and B means and variances from the corresponding pixel value history stored in buffer F o from c R G B and w Mes Hy calculate a ratio of similarity between the current pixel R G and B values and their calculated means T Sanga 21 el see appendix A for a graphical derivation of equation 21 When the current pixel is more intensive than it s mean S will be greater than 1 if the current pixel is darker S will always be between 0 and 1 o if Sp lt 1 and Spo gt T then the current pixel is a shadow pixel else the pixel is a pure foreground pixel o Update mask at corresponding location write value 255 for pure foreground pixel write 125 for shadow pixel and continue with next pixel in the current frame 23 5 3 2 Shadow detector for the
55. r The value for s ranges from O to 1 and represents 7 see section 5 3 1 If this parameter is set to a relatively low value a lot of foreground pixels will be classified as shadow pixels If s is set to 1 the shadow detector is disabled since the 31 condition if Spc lt 1 and S gt T will never be valid for any pixel values s is also a scene dependent parameter For the videos A B and C the values are set to 0 4 The number of history frames Sf is used to construct a mean of pixel values at each location in the scene This results a more robust way to see if a pixel is indeed for example darker for a certain period of time In this research we use a default value of 15 shadow history frames Statistical implementation The essential parameters in the statistical implementation are the size of the frame buffer number of history elements to consider T the threshold on the squared Mahalanobis distance to decide if it is well described by the background model or not c and finally the threshold 7 for the shadow darkness s The value for T determines how fast the weight of a Gaussian component changes when a new sample is processed A high value for T means that the speed of update slows down In traffic scenes where a robust background model is needed the speed of update cannot be too fast and therefore this parameter is set for all videos to 1000 This means that 0 001 is added if the new sample matched this component or
56. ras at different intersections producing video streams o Camera initialisation and calibration by defining interesting regions in the image frames for each camera and set a distance correction for each camera geometry o For each stream o Acomponent that performs background subtraction and that eliminates shadows o Acomponent capable of tracking each foreground object in the stream o Acomponent capable of extracting traffic parameters for each foreground object in the stream A component recording the streams when necessary video summarization A central component which combines and stores traffic parameters from multiple streams o A GUI showing the flow of the traffic and which can query upon the gathered data In figure 2 on page 7 the relation between the components of the system is sketched where arrows represent the direction of the processing steps The boxes that are present in the red square are components that belong to the background subtraction component Now all the components defined in this framework figure 2 will be explained in some more detail Camera units We aim here at using cheap low resolution greyscale or colour cameras which can be compared to simple webcams These cameras produce frames at a fixed frame rate which are the input data for the system Camera initialisation and calibration In the initialisation step interesting points in the static scene should be manually marked once Interesting points could be area
57. rrors made in the past and the rate of uncertainty should be minimized A number of versions of Kalman filters are used in literature The simplest version 10 uses only the luminance intensity but more complex versions also take into account the temporal derivative or spatial derivatives The internal state of the system is then described by the background intensity B and its derivative B These two features are recursively updated using a particular scheme 17 In general background modelling using Kalman filters performs not very well since the model is easily affected by the foreground pixels causing long trails after moving objects 15 17 o Mixture of Gaussians MoG The MoG method tracks multiple Gaussian distributions simultaneously unlike the Kalman filter which tracks the evolution of only a single Gaussian This method is very popular since it also capable of handling multi modal background distributions as described above at the non parametric approach Similar to this non parametric model MoG maintains a density function for each pixel On the other hand MoG is parametric the model parameters can be adaptively updated without keeping a large buffer of video frames 17 26 27 28 The disadvantages of this model is that it is computationally intensive and its parameters require careful tuning It is also very sensitive to sudden changes in global illumination it can happen that the entire video frame will be classified as for
58. s becomes to large when considering a model based approach it is always better to use a complete deterministic approach For indoor environments the statistical approaches are the more reliable since the scene is constant and a statistical description is very effective Conclusions of Prati et a are that a statistical parametric spatial approach better distinguishes between moving shadows and moving objects in contrast to deterministic approaches and a deterministic non model based approach detects moving shadows better in contrast to statistical approaches So a deterministic non model based approach gives good detection while a statistical parametric approach gives good discrimination 5 Implementations This chapter describes the two implemented background subtraction algorithms in this research project 5 1 and 5 2 The implementation of two shadow detectors will be discussed in 5 3 one suitable for the first subtraction algorithm and one for the other In 5 4 we describe how we applied a simple video summarization to obtain compact videos only containing moving traffic Although the focus of this research is not on video summarization it is a nice application to test the two implemented background subtraction algorithms Finally 5 5 describes the steps to actually work with the created software and its wrapper The software constructed in this research is written using Microsoft Visual C 6 0 and the Open Source Computer Vision Library O
59. s where new vehicles can appear or disappear Another possibility is to mark only the roads themselves as interesting areas if you are only interested in vehicles on the road Calibration of the camera means that the relative transition from image coordinates to world coordinates is known This is important for the system to derive speeds and locations of vehicles in the scene Background subtraction This component is responsible for generating a background model and a binary mask that displays all foreground objects in the current frame Also incorporated in this component is the detection and elimination of shadows This research emphases on these features In the next chapter the steps in background subtraction will be described together with existing algorithms and methods used in literature Tracking foreground objects The foreground objects found by the background subtraction in each frame should be tracked through time This part is responsible for identifying and labelling the foreground objects in successive frames Update traffic parameters This component should gather information about each labelled and tracked foreground object such as speed location acceleration size type of object etc This information is then send for each frame to the central processing Video Summarization For later analyses of the traffic at intersections this component is responsible for recording video only when interesting events at the intersec
60. s will occur So using the median approach always results in better initial performance in this setting In figure 14 the frame N 1 is shown for both mean and median approaches On this frame background subtraction as described in 5 1 is applied and the white pixels correspond to the foreground pixels Since there is still no traffic visible these pixels are wrongly classified From both images it can be seen that the median approach of creating the background model performs better since a lower number of wrongly classified foreground pixels was found my 1 avi 640x480 W AVI File C vid my1 avi 640x480 foreground pixels 310 foreground pixe y f Figure 13 Processed frame N 1 from video A using initial background constructed using the mean approach left and using the median approach right In the left image a number of foreground pixels are clearly visible in the area of the upper left tree The next parameter of interest is the subtraction threshold parameter T This is the value for T in pseudo algorithm A1 By setting T to a relatively low value the probability of detecting the foreground objects increases but also the number of misclassifications in the background increases when the lightning conditions change just a bit The value for T is highly scene dependent For our recorded videos A B and C this value is set to 40 The values set for the two parameters s and sf determine the performance of the shadow detecto
61. scene The video summarization system described above will start recording as soon as already a significant part of the vehicle is visible in the scene depending on the value given for parameter R Since we are interested in the recording of the full path of the vehicle in the scene we extended the given basic idea by keeping a buffer of history frames present which can be written to the summarization video when this is necessary In analogy when the car will leave the scene the basic system will already stop recording when the car is still partly visible In the extended implementation the recording will stop when the car has completely disappeared from the scene The flowchart in figure 12 shows how this extension of the basic system is implemented This extended video summarization can be activated by using the Rb parameter instead of the R parameter See section 5 5 for more information 25 5 5 How to use the software To compile the two background subtraction implementations open the corresponding project files with Visual C and make sure that these include paths are set in tools gt options gt directories gt include files e lt opencv directory gt cv include e lt opencv directory gt cxcore include e lt opencv directory gt otherlibs cvcam include e Kopencv directory gt otherlibs highgui e lt source code directory gt bloblib Also make sure that the following libraries are set in tools gt options gt directories
62. smoothing Smoothing can also be used to eliminate environmental noise such as rain and snow captured by the camera When applying a spatial smoothing all the pixels in one frame are smoothed based on the values of surrounding pixels In this way the image softens When temporal smoothing is applied the value of a pixel at a particular location and surrounding locations is averaged with the same pixel s from the next or multiple next past frames This tends to smooth motion Background subtraction background modelling scene 1 5 5 3 a data validation data validation and shadow and shadow elimination in elimination in Figure 2 Schematic overview of a general traffic monitoring system To make sure a traffic system runs in real time the frame rate can be reduced in this stage by not processing each frame Also a reduction of camera resolution is possible by rescaling each incoming image frame Another important issue in this first stage is how to store the data of the image frames from the stream Some algorithms only handle luminance intensity which requires only one scalar value for each pixel in the image In this case only a black and white video stream is needed However when using information of each colour channel in coloured frames better subtraction results can be obtained 9 14 especially when objects in the scene are present in low contrast areas Modern cameras equipped with an CCD sensor linearly trans
63. ssume that the value for T in A1 is set to 60 Now we monitor a single pixel in a number of frames coloured red in figure 5 In figure 5A the pixel is clearly classified as background and thus the pixel at the same location in the background model is updated and again set to O In figure 5B one frame later the pixel of interest is again classified as background 35 lt T and the corresponding pixel in the background model is now set to 35 3 In the next frame figure 5C the pixel is still classified as background since 70 35 3 lt T As a result of this error the corresponding pixel in the background model will be updated with a higher value Now suppose that the object stands still for a number of frames in situation c Every time the pixel will be classified as background and the value for the background pixel in the model will become higher and higher Now suppose the object moves on after a while situation d I x y will be O but the corresponding pixel in the background model has become gt T Therefore the pixel in situation d will be classified as a foreground pixel Pixels in the neighbourhood of our pixel of interest also have a high probability to undergo the same process as described In this way clusters of undesired foreground pixels will occur Figure 6 shows an example of the effect of not including the concatenation of I x v B x y k conditions A car entered the scene from the bottom right and clearly a trail
64. ted by these systems In this Master thesis a much cheaper and versatile traffic monitor system is proposed using a vision based approach In this setting traffic at intersections in cities are monitored using simple cameras webcams that are located on a high spot somewhere near these intersections A typical video frame obtained from such cameras is displayed in figure 1 Most existing research and applications on traffic monitoring focus on monitoring cars on highways As will become clear in chapter 2 successful Figure 1 A typical intersection in an urban area monitoring traffic at crossroads in where a webcam is placed at some high location crowded urban areas is far more difficult Using a vision based approach for monitoring the mentioned drawbacks of classic systems no longer exist Further more the flow of the traffic in the city can easily be displayed at a central place by combining information from each video stream Although a full framework of the proposed monitoring system will be given this research focuses mainly on one component in the framework the background subtraction and shadow elimination An extensive overview of methods and algorithms concerning background subtraction and shadow elimination will be discussed in the literature section Two different background subtraction algorithms suitable for our urban setting are actually implemented a statistical and a deterministic approach and compared The algorit
65. the background subtraction phase is to improve the results we obtained in the form of a binary mask chapter 4 3 The background models described in chapter 4 2 have these general drawbacks 17 1 All models ignore any correlation between neighbouring pixels 2 The rate of adaption may not match the moving speed of the foreground objects This is especially the case in an urban setting since cars with all kinds of speeds will pass by 3 In most of the models Non stationary pixels from moving leaves or shadow cast by moving objects are easily mistaken as true foreground objects As a result of the first drawback a lot of small false positives and false negatives will occur distributed across the candidate mask One solution for this problem is to discard all classified foreground objects which have such a small size that the object can never be a vehicle or pedestrian For this a connected component grouping algorithm can be used Using morphological filtering like dilation and erosion on foreground masks eliminates isolated foreground pixels and merges nearby disconnected foreground regions As a result of the second drawback it can happen that large areas of false foreground also called ghosts might be visible in the binary mask 22 This happens when the background model adapts at a slower rate than the foreground scene A solution to this problem is to use multiple background models running at different adaptation rates and periodic
66. tions In this way components can be active or inactive dynamically Components are removed as soon as their weights become negative A more detailed description of this dynamic class size determination can be found in Zivkovic s work 28 Since prior evidence was introduced by the authors the update equation for the weight of a distribution will become C Op Okt t a E 5 4 a 20 where T is again the number of history elements in the history buffer and c denotes the prior evidence As can be seen from equation 20 the distribution weights can now become negative Zivkovic uses the same update equations for the mean and covariance of the distributions equations 16 and 17 For the implementation of this statistical approach the C library from the author of 28 is used which performs the background subtraction the way it was described above A wrapper for this library is created in such a way that the basic command line parameters match those of the implementation of the deterministic background model described in 5 1 To summarize the statistical background subtraction algorithm a flowchart is depicted in figure 9 which describes the process of the system Note that the number of samples per distribution is limited to the size of the buffer of history pixel values that is used The buffer operations are not included in the chart for including them makes the chart unnecessary complex Also note that processes within the red
67. tions occur So for example when there is no traffic at all no video needs to be recorded Central processing Receives traffic parameters from all available streams and stores them in a database for easy and fast retrieval Parallelization is of great importance since data from different streams arrive simultaneously GUI The GUI is responsible for letting the operator calibrate and initialize the cameras Furthermore is should display the flow of traffic through the city So at the intersections information about the vehicles should be displayed by retrieving the traffic parameters from the central processing database 4 Literature on background subtraction algorithms Most of the background subtraction algorithms found in literature have a generic flow which consists of four stages 17 pre processing background modelling foreground detection and data validation Each of these steps will be discussed in some detail in the next section For traffic monitoring eliminating shadows in video frames is also very important Therefore this step of shadow detection and elimination is incorporated in the background subtraction component 4 1 Pre processing In the pre processing step the raw data stream coming from the camera is processed in such a way that the format can be handled by the visual based components of the system In this early stage of processing techniques are often used to reduce camera noise using simple temporal or spatial
68. ubtraction technique uses the current pixel values in a video frame to update the background model To make sure that moving foreground objects are not registered in the background model only the pixels classified as background are updated Classifying is done by considering the distance between pixel values in the background model and the new incoming video frame The second implemented technique is a statistical approach which models each pixel in the background model by a Gaussian mixture model Updating the model is done by a number of update equations that update the weight mean and variance of each Gaussian component in the model Classifying a new pixel is done by extracting those Gaussian components in the model that have high weights and low variances which are the indicators of being a background component Both algorithms are evaluated using an implemented video summarization technique The recorded videos that were used for the evaluation are 3 scenes of the same crossroad under different weather conditions From this evaluation it can be concluded that the statistical approach has a better performance in all cases No matter the weather conditions the statistical approach outperforms the deterministic approach The deterministic algorithm has difficulties with rainy scenes and scenes in which a lot of branches of trees are moving in the wind The statistical approach deals better with this problem since multiple Gaussian components can act as
69. xcludes all frames where no traffic was present at all To determine if the current frame needs to be stored we simply count the number of pixels in the binary mask M x y that have a value of 255 classified as a pure foreground pixel If this number of pixels exceeds the value R or Rb the current frame is written to the movie file on disk The choice of R or Rb is scene dependent For example if a lot of branches of trees in the scene are moving in the wind which results in some foreground misclassifications a higher value for this parameter is desired All the frames that are stored by the video summarization system are labelled by a second precision timestamp see figure 10 and appended to the output movie file In this way when the recorded result is watched later the time intervals in which no traffic was present can be determined easily The output file can be written to disk using compression in real time The sample files we produced in this research are compressed using the XVID codec which only requires on average 1 5 MB for a minute of video in 640x480 If the video summarization option is enabled by the user a codec for compression can be selected from a window The output file will be stored in the same directory as the main executable and the filename for the video has the format yyyy mm dd hh mm ss avi Figure 10 Recorded video frame labelled Wed Oct 04 1504452006 with a timestamp Now suppose one car enters the

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