Tracking Vehicles in the Presence of Occlusions
doi: 10.14456/mijet.2018.10
Keywords:
Vehicle tracking, occlusions, traffic violations, red lightsAbstract
Thailand loses about 25,000 people every year to road trauma: this motivated this study, which designed and evaluated a simple system to discourage ‘Red Light Running’ – failure to observe red traffic lights. Our system is simple, cheap and flexible – consisting of a single camera, a portable computer and the ability to send images to a mobile phone using the public network. However, to be flexible, cameras were set only 1-2m about the road, which caused many occlusions to be observed – the major challenge for the system software. A rule-based system was used to resolve most occlusions. In our tests, vehicles were completely and correctly tracked in 83% of frames. This was sufficient to allow images of 95% of Red Light Runners to be transmitted to a monitoring station and potentially stopped.Thailand loses about 25,000 people every year to road trauma: this motivated this study, which designed and evaluated a simple system to discourage ‘Red Light Running’ – failure to observe red traffic lights. Our system is simple, cheap and flexible – consisting of a single camera, a portable computer and the ability to send images to a mobile phone using the public network. However, to be flexible, cameras were set only 1-2m about the road, which caused many occlusions to be observed – the major challenge for the system software. A rule-based system was used to resolve most occlusions. In our tests, vehicles were completely and correctly tracked in 83% of frames. This was sufficient to allow images of 95% of Red Light Runners to be transmitted to a monitoring station and potentially stopped.
References
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[36] SMEULDERS, A. CHU, D., CUCCHIARA, R., CALDERARA, DEHGHAN, A., SHAH, M. Visual Tracking: An Experimental Survey. IEEE TPAMI, vol. 36, pp. 1442-1468, 2013.
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[38] KRISTAN, M. et al., The Visual Object Tracking VOT2015 Challenge Results. Visual Object Tracking Workshop IEEE Intl Conf. Computer Vision Workshop (ICCVW), 2015.
[39] NAM. H. HAN, B. Learning multi-domain convolutional neural networks for visual tracking. Proc IEEE Conf. Computer Vision Pattern Recognition, pp 4293-4302, 2016.
[40] KRISTAN, M. et al. The Visual Object Tracking (VOT2018) Challenge Results,” ECCVW, pp. 1–27, 2018.
[41] LEE, B., LIEW, L., CHEAH, W., WANG, Y. Occlusion handling in videos object tracking. IOP Conf. Series Earth Env Science. vol. 18 (1), 2014.
[42] ZHAO S, ZHANG S, ZHANG L. Towards Occlusion Handling: Object Tracking With Background Estimation. IEEE Trans Cybern., vol. 48(7), pp. 2086-2100, 2018.
[43] MOTRO, M., GHOSH, J. Measurement-wise Occlusion in Multi-object Tracking, arXiv preprint arXiv:1805.08324. 2018.
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[45] DOYLE, D., ALAN L. JENNINGS, A., BLACK, J. Optical flow background estimation for real-time pan/tilt camera object tracking. Measurement, vol. 48, pp 195–207, 2014.
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[47] OCCAM, W. Occam’s Razor, https://en.wikipedia.org/
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[50] SEIDEL, F., HAGE, C., KLEINSTEUBER, M. pROST: a smoothed ℓp-norm robust online subspace tracking method for background subtraction in video. Machine Vision and Applications, vol. 25(5), pp. 1227--1240, 2014.
[51] LI, X., WANG, K., WANG, W., LI, Y. A Multiple Object Tracking Method using Kalman Filter. Inf. Autom., vol. 1(1), pp. 1862–1866, 2010.
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dd/d6a/classcv_1_1KalmanFilter.html. [Accessed: 07-Nov-2018].
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[54] SMARTVISION TECHNOLOGY CO. LTD. Red Light Camera (RLC). www.smartvisoncompany.com/
/redlightcamera.html [Accessed: 08-Nov-2018]
[55] KLUBSUWAN, K., KOODTALANG, W., MUNGSING, S. Traffic violation detection using multiple trajectories evaluation of vehicles. Proc. - Int. Conf. Intell. Syst. Model. Simulation, ISMS, vol. 5 (12), pp. 220–224, 2013.
[56] K. KIM, T. CHALIDABHONGSE, D. HARWOOD, AND L. DAVIS, Real-time foreground background segmentation using codebook model. Real Time Imaging, vol. 11(3), pp. 172–185, 2005
[57] MENG, C. Traffic Violation Detection. MEng thesis, Faculty of Engineering, Mahasarakham University, 2018.
[58] REDDY, V. SANDERSON, C., LOVELL, B. Improved foreground detection via block-based classifier cascade with probabilistic decision integration. IEEE Trans. Circuits Syst. Video Technol., vol. 23(1), pp. 83–93, 2013.
[59] WREN, R., AZARBAYEJANI, A., DARRELL, T., AND PENTLAND, A. Pfinder: Real-time tracking of the human body. IEEE TPAMI, vol. 19(7), pp. 780–785, 1997.
[60] KAEWTRAKULPONG, P. BOWDEN, R. An Improved Adaptive Background Mixture Model for real-time tracking with shadow detection. Video-Based Surveillance Systems, Springer, pp. 135–144, 2002.
[61] RODRIGUEZ. P. B. WOHLBERG, B. Fast principal component pursuit via alternating minimization. Proc. IEEE Int. Conf. Image Process. pp. 69–73, Sep 2013.
[2] ECONOMIC CONDITIONS AND AUSTRALIAN AUTOMOBILE ASSOCIATION, Cost of Road Trauma in Australia, Australian Automobile Association, 2015.
[3] ROYAL THAI POLICE (2013), loc. cit. WHO and Bloomberg Initiative for Global Road Safety. Road Safety Institutional and Legal Assessment Thailand. http://www.searo.who.int/thailand/
areas/rs-legal-eng11.pdf, 2015..
[4] RETTING, R., WILLIAMS, A., GREENE, M. Red-Light Running and Sensible Countermeasures: Summary of Research Findings. Transp. Res. Rec. J. Transp. Res. Board, vol. 1640, p. 23–26, Jan. 1998.
[5] WORLD HEALTH ORGANIZATION, “Global status report on road safety,” Inj. Prev., p. 318, 2013.
[6] CHOUDHURY, S.K., SA, P. K., S. BAKSHI, S., B. MAJHI. An Evaluation of Background Subtraction for Object Detection Vis-a-Vis Mitigating Challenging Scenarios, IEEE Access, vol. 4, p. 6133–6150, 2016.
[7] BABAEE, M., DINH, D., RIGOLL, G. A deep convolutional neural network for video sequence background subtraction. Pattern Recognition, vol 76, p. 635-649, 2018.
[8] HARITAOGLU, L.,. HARWOOD, D., AND L. S. DAVIS, L. W4: Real-time surveillance of people and their activities. IEEE TPAMI, vol. 22(8), pp. 809–830, 2000.
[9] STAUFFER, C., GRIMSON, W. Adaptive background mixture models for real-time tracking. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2246–2252, 1999.
[10] ZIVKOVIC, Z., VAN DER HEIJDEN, F. Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognit. Lett., vol. 27(7), pp. 773–780, 2006.
[11] OpenCV: cv::BackgroundSubtractorKNN Class Reference.” [Online]. Available: http://docs.opencv.org/trunk/db/d88/classcv_1_1BackgroundSubtractorKNN.html. [Accessed: 02-Sep-2017].
[12] XU, Y., DONG, J,. ZHANG, B., XU, D. Background modeling methods in video analysis: A review and comparative evaluation, CAAI Trans. Intel. Technology, 1, pp 43-60, 2016.
[13] PICCARDI, M., T JAN, T. Mean-shift background image modelling. Int Conf Image Processing, Singapore, 3399-3402, 2004
[14] ELGAMMAL, A., DURAISWAMI, R., DAVID HARWOOD, D., DAVIS, L. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, Proceedings of the IEEE, vol. 90(7), pp. 1151-1163, 2002.
[15] MITTAL. A., PARAGIOS, N. Motion-Based Background Subtraction using Adaptive Kernel Density Estimation. IEEE Conf Comp Vision Pat Recog, pp. 302-309, 2004.
[16] LO, B., VELASTIN, S., Automatic congestion detection system for underground platforms. Proc. 2001 Intl Symp on Intelligent Multimedia, Video and Speech Processing, Hong Kong, pp. 158-161, 2001.
[17] WANG, H., SUTER, D. A consensus-based method for tracking: Modelling background scenario and foreground appearance. Pattern Recognition, 40(3), pp. 1091-1105, 2007.
[18] EL BAF, F., BOUWMANS, T., VACHON, B. A Fuzzy approach for background subtraction. ICIP 2008: pp. 2648-2651, 2008.
[19] CULIBRK, D., MARQUES, O., SOCEK, D., KALVA, H. FURHT, B. Neural Network Approach to Background Modeling for Video Object Segmentation. IEEE Trans Neural Networks, vol. 18(6), pp. 1614-1627, 2007.
[20] YAO, J., ODOBEZ, J. Multi-layer background subtraction based on color and texture. IEEE Comp Vision and Pattern Recognition, pp 1-8, 2007.
[21] ZHOU, T TAO, D. GoDec: Randomized low-rank & sparse matrix decomposition in noisy case. Intl Conf on Machine Learning, pp 33-40, 2011.
[22] HARDAS, A., BADE D., WALI,V. Moving Object Detection using Background Subtraction, Shadow Removal and Post Processing, IJCA Proceedings on International Conference on Computer Technology, pp. 975–8887, 2015.
[23] SUBUDHI, B., GHOSH, S., SHIU, S., GHOSH, A. Statistical feature bag based background subtraction for local change detection. Inf. Sci. (NY)., vol. 366, pp. 31–47, 2016.
[24] SAJID, H. CHEUNG, S. Background subtraction for static & moving camera. ICIP 2015: 4530-4534, 2015.
[25] ibid. Universal Multimode Background Subtraction. IEEE Trans Image Processing, vol 26(7), 3249-3260, 2017.
[26] WANG, Y, JODOIN, P., PORIKLI, F., KONRAD, J., BENEZETH, Y., ISHWAR, P. CDnet 2014: An Expanded Change Detection Benchmark Datase. Proc IEEE Conf. Computer Vision and Pattern Recognition Workshops, pp 393-400, 2014
[27] JEEVA, S., SIVABALAKRISHNAN, M. Survey on background modeling and foreground detection for real time video surveillance. Procedia Comput. Sci., vol. 50, pp. 566–571, 2015.
[28] HOFMANN, M., TIEFENBACHER, P., RIGOLL, G. Background Segmentation with Feedback: The Pixel-Based Adaptive Segmenter. IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38-43, 2012
[29] MADDALENA L., PETROSINO, A. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Trans Image Process., vol 17(7), pp 1168-1177, 2008.
[30] BARNICH, O., VAN DROOGENBROECK, M. ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans. Image Process., vol. 20(6), pp. 1709–1724, 2011.
[31] LEE, J., PARK, M.. An Adaptive Background Subtraction Method Based on a Kernel Density Estimation. Sensors, vol 12, pp 12279-12300, 2012.
[32] SOOKSATRA, S. T. KONDO, T. Red traffic light detection using fast radial symmetry transform. 2014 11th Intl Conf Electrical Engineering/ Electronics, Computer, Telecom and Inf Tech, ECTI-CON 2014, 2014.
[33] GUO, H., GAO, Z., YIANG, X. JIANG, X. Modeling Pedestrian Violation Behavior at Signalized Crosswalks in China: A Hazards - Based Duration Approach. Traffic Injury Prevention, vol. 12, 96-103, 2011.
[34] DIAZ, M., CERRI, P., PIRLO, G., FERRER, M. A Survey on Traffic Light Detection. New Trends in Image Analysis and Processing - ICIAP 2015 Workshops, 201-208, 2015.
[35] YILMAZ, A., JAVED, O., SHAH, M. Object tracking: A survey. ACM Comput. Surv. 38 (4), 13, 2006.
[36] SMEULDERS, A. CHU, D., CUCCHIARA, R., CALDERARA, DEHGHAN, A., SHAH, M. Visual Tracking: An Experimental Survey. IEEE TPAMI, vol. 36, pp. 1442-1468, 2013.
[37] WU, Y., LIM, J., YANG, M.. (2015). Object Tracking Benchmark. IEEE TPAMI, vol. 37(9). pp.1834-1848, 2013. doi: 10.1109/TPAMI.2014.2388226.
[38] KRISTAN, M. et al., The Visual Object Tracking VOT2015 Challenge Results. Visual Object Tracking Workshop IEEE Intl Conf. Computer Vision Workshop (ICCVW), 2015.
[39] NAM. H. HAN, B. Learning multi-domain convolutional neural networks for visual tracking. Proc IEEE Conf. Computer Vision Pattern Recognition, pp 4293-4302, 2016.
[40] KRISTAN, M. et al. The Visual Object Tracking (VOT2018) Challenge Results,” ECCVW, pp. 1–27, 2018.
[41] LEE, B., LIEW, L., CHEAH, W., WANG, Y. Occlusion handling in videos object tracking. IOP Conf. Series Earth Env Science. vol. 18 (1), 2014.
[42] ZHAO S, ZHANG S, ZHANG L. Towards Occlusion Handling: Object Tracking With Background Estimation. IEEE Trans Cybern., vol. 48(7), pp. 2086-2100, 2018.
[43] MOTRO, M., GHOSH, J. Measurement-wise Occlusion in Multi-object Tracking, arXiv preprint arXiv:1805.08324. 2018.
[44] MILAN, A., LEAL-TAIXE, L., SCHINDLER, R., CREMERS, D., ROTH, S., REID, I. Multiple Object Tracking Benchmark. https://motchallenge.net, Accessed 23.10.2018.
[45] DOYLE, D., ALAN L. JENNINGS, A., BLACK, J. Optical flow background estimation for real-time pan/tilt camera object tracking. Measurement, vol. 48, pp 195–207, 2014.
[46] Kale, K., Pawar, S., Dhulekar, A. Moving Object Tracking using Optical Flow and Motion Vector Estimation. 4th Intl Conf. Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), pp 1-6, 2015.
[47] OCCAM, W. Occam’s Razor, https://en.wikipedia.org/
wiki/Occam’s_razor. Accessed 7 Nov, 2018.
[48] SHIMADA, A., NAGAHARA, H., TANIGUCHI, R. Background modeling based on bidirectional analysis. Computer Vision Pattern Recognition, pp. 1979–1986, 2013.
[49] HAINES, T., XIANG, T, Background Subtraction with DirichletProcess Mixture Models. IEEE Trans Pattern Analysis Machine Intelligence, vol. 36(4), pp. 670-683, 2014. doi: 10.1109/TPAMI.2013.239.
[50] SEIDEL, F., HAGE, C., KLEINSTEUBER, M. pROST: a smoothed ℓp-norm robust online subspace tracking method for background subtraction in video. Machine Vision and Applications, vol. 25(5), pp. 1227--1240, 2014.
[51] LI, X., WANG, K., WANG, W., LI, Y. A Multiple Object Tracking Method using Kalman Filter. Inf. Autom., vol. 1(1), pp. 1862–1866, 2010.
[52] OPENCV: cv::KalmanFilter Class Reference. [Online]. Available: https://docs.opencv.org/3.4/
dd/d6a/classcv_1_1KalmanFilter.html. [Accessed: 07-Nov-2018].
[53] JENOPTICK AG. Flexible systems and services for Traffic Safety. 2016. [Online]. Available: https://www.jenoptik.com/products/traffic-safety-systems/combined-speed-redlight-enforcement-monitoring. [Accessed: 08-Nov-2018].
[54] SMARTVISION TECHNOLOGY CO. LTD. Red Light Camera (RLC). www.smartvisoncompany.com/
/redlightcamera.html [Accessed: 08-Nov-2018]
[55] KLUBSUWAN, K., KOODTALANG, W., MUNGSING, S. Traffic violation detection using multiple trajectories evaluation of vehicles. Proc. - Int. Conf. Intell. Syst. Model. Simulation, ISMS, vol. 5 (12), pp. 220–224, 2013.
[56] K. KIM, T. CHALIDABHONGSE, D. HARWOOD, AND L. DAVIS, Real-time foreground background segmentation using codebook model. Real Time Imaging, vol. 11(3), pp. 172–185, 2005
[57] MENG, C. Traffic Violation Detection. MEng thesis, Faculty of Engineering, Mahasarakham University, 2018.
[58] REDDY, V. SANDERSON, C., LOVELL, B. Improved foreground detection via block-based classifier cascade with probabilistic decision integration. IEEE Trans. Circuits Syst. Video Technol., vol. 23(1), pp. 83–93, 2013.
[59] WREN, R., AZARBAYEJANI, A., DARRELL, T., AND PENTLAND, A. Pfinder: Real-time tracking of the human body. IEEE TPAMI, vol. 19(7), pp. 780–785, 1997.
[60] KAEWTRAKULPONG, P. BOWDEN, R. An Improved Adaptive Background Mixture Model for real-time tracking with shadow detection. Video-Based Surveillance Systems, Springer, pp. 135–144, 2002.
[61] RODRIGUEZ. P. B. WOHLBERG, B. Fast principal component pursuit via alternating minimization. Proc. IEEE Int. Conf. Image Process. pp. 69–73, Sep 2013.
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2018-12-31
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Morris, J., Meng, C., & Suwannata, N. . (2018). Tracking Vehicles in the Presence of Occlusions: doi: 10.14456/mijet.2018.10. Engineering Access, 4(2), 56–67. Retrieved from https://ph02.tci-thaijo.org/index.php/mijet/article/view/10.14456.mijet.2018.10
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