Gaussian Mixture Model Based Moving Object Detection and Tracking for Traffic Surveillance
Keywords:
Gaussian mixture model, Moving object detection, tracking, morphological operations, blob analysis
Abstract
Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting the moving objects in digital videos. The moving detection and tracking system is developed based on Gaussian Mixture Model (GMM) estimation together with applicable, and combination of various relevant computer vision and image processing techniques for enhancing the process. To remove noises a median filter is used, and by applying thresholding algorithms via morphological operations the unwanted objects are removed. In addition, the object type restrictions are set using blob analysis. The results showed that the proposed system can successfully detect and track the moving objects in urban videos.
References
[1] J. L. Barron, D. J. Fleet and S. S. Beauchemin, "Systems and experiment performance of optical flow techniques," International journal of computer vision, vol. 12, no. 1, pp. 43-77, 1994.
[2] M. J. Black and a. P. Anandan, "The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields," Computer vision and image understanding, vol. 63, no. 1, pp. 75-104, 1996.
[3] Rakshit, Subrata and a. C. H. Anderson, "Computation of optical flow using basis functions," IEEE transactions on image processing, vol. 6, no. 9, pp. 1246-1254, 1997.
[4] Huang, ZhaoNan, H. Qin and a. Q. Liu, "Vehicle ROI extraction based on area estimation gaussian mixture model," in 3rd IEEE International Conference on Cybernetics, 2017.
[5] Zhan, Wei and a. X. Ji, "Algorithm Research on Moving Vehicles Detection," in Procedia Engineering 15, 2011.
[6] Ballard and D. H., "Generalizing the Hough transform to detect arbitrary shapes," Pattern recognition, vol. 13, no. 2, pp. 111-122, 2001.
[7] J. Illingworth, Kittler and a. Josef, "A survey of the Hough transform," Computer vision, graphics, and image processing, vol. 44, no. 1, pp. 87-116, 1988.
[8] J. Wei, C. Song and a. P. Shouyan, "Moving objects detection algorithm in video sequence with improved GMM," Journal of Chongqing Jiaotong University (Natural Science), vol. 2, 2013.
[9] X. Shi, "Research on Moving Object Detection Based on Optical Flow Mechanism," in University of Science and Technology of China, 2010.
[10] Tang, Zhen, Z. Miao and a. Y. Wan, "Background subtraction using running Gaussian average and frame difference," International Conference on Entertainment Computing, pp. 411-414, 2007.
[11] F. Nir and a. S. Russell, "Image segmentation in video sequences: A probabilistic approach," in Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., 1997.
[12] C. a. W. E. L. G. Stauffer, "Adaptive background mixture models for real-time tracking," Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 2, pp. 246-252, 1999.
[13] E. How-Lung and a. K.-K. Ma, "Noise adaptive soft-switching median filter," IEEE Transactions on image processing, vol. 10, no. 2, pp. 242-251, 2001.
[14] T. Sofia, C. Kotropoulos and a. I. Pitas, "Morphological signal adaptive median filter for still image and image sequence filtering," ISCAS'98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, vol. 4, pp. 21-24, 1998.
[15] Gonzalez, “Digital image processing,” Prentice Hall Press, 2002.
[16] Tang, S. Ling, Z. Kadim, K. M. Liang and a. M. K. Lim, "Hybrid blob and particle filter tracking approach for robust object tracking," Procedia Computer Science, vol. 1, no. 1, pp. 2549-2557, 2010.
[17] Telagarapu, Prabhakar, M. N. Rao and a. G. Suresh, "A novel traffic-tracking system using morphological and Blob analysis," 2012 International Conference on Computing, Communication and Applications, pp. 1-4, 2012.
[18] Gangodkar, Durgaprasad, P. Kumar and a. A. Mittal, "Robust Segmentation of Moving Vehicles Under Complex Outdoor Conditions," IEEE Transactions on intelligent transportation systems, vol. 13, no. 4, pp. 1738-1752, 2012.
[2] M. J. Black and a. P. Anandan, "The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields," Computer vision and image understanding, vol. 63, no. 1, pp. 75-104, 1996.
[3] Rakshit, Subrata and a. C. H. Anderson, "Computation of optical flow using basis functions," IEEE transactions on image processing, vol. 6, no. 9, pp. 1246-1254, 1997.
[4] Huang, ZhaoNan, H. Qin and a. Q. Liu, "Vehicle ROI extraction based on area estimation gaussian mixture model," in 3rd IEEE International Conference on Cybernetics, 2017.
[5] Zhan, Wei and a. X. Ji, "Algorithm Research on Moving Vehicles Detection," in Procedia Engineering 15, 2011.
[6] Ballard and D. H., "Generalizing the Hough transform to detect arbitrary shapes," Pattern recognition, vol. 13, no. 2, pp. 111-122, 2001.
[7] J. Illingworth, Kittler and a. Josef, "A survey of the Hough transform," Computer vision, graphics, and image processing, vol. 44, no. 1, pp. 87-116, 1988.
[8] J. Wei, C. Song and a. P. Shouyan, "Moving objects detection algorithm in video sequence with improved GMM," Journal of Chongqing Jiaotong University (Natural Science), vol. 2, 2013.
[9] X. Shi, "Research on Moving Object Detection Based on Optical Flow Mechanism," in University of Science and Technology of China, 2010.
[10] Tang, Zhen, Z. Miao and a. Y. Wan, "Background subtraction using running Gaussian average and frame difference," International Conference on Entertainment Computing, pp. 411-414, 2007.
[11] F. Nir and a. S. Russell, "Image segmentation in video sequences: A probabilistic approach," in Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., 1997.
[12] C. a. W. E. L. G. Stauffer, "Adaptive background mixture models for real-time tracking," Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 2, pp. 246-252, 1999.
[13] E. How-Lung and a. K.-K. Ma, "Noise adaptive soft-switching median filter," IEEE Transactions on image processing, vol. 10, no. 2, pp. 242-251, 2001.
[14] T. Sofia, C. Kotropoulos and a. I. Pitas, "Morphological signal adaptive median filter for still image and image sequence filtering," ISCAS'98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, vol. 4, pp. 21-24, 1998.
[15] Gonzalez, “Digital image processing,” Prentice Hall Press, 2002.
[16] Tang, S. Ling, Z. Kadim, K. M. Liang and a. M. K. Lim, "Hybrid blob and particle filter tracking approach for robust object tracking," Procedia Computer Science, vol. 1, no. 1, pp. 2549-2557, 2010.
[17] Telagarapu, Prabhakar, M. N. Rao and a. G. Suresh, "A novel traffic-tracking system using morphological and Blob analysis," 2012 International Conference on Computing, Communication and Applications, pp. 1-4, 2012.
[18] Gangodkar, Durgaprasad, P. Kumar and a. A. Mittal, "Robust Segmentation of Moving Vehicles Under Complex Outdoor Conditions," IEEE Transactions on intelligent transportation systems, vol. 13, no. 4, pp. 1738-1752, 2012.
Published
2020-03-01
How to Cite
Kadkhodaei Elyaderani, S., & Nasri, S. (2020). Gaussian Mixture Model Based Moving Object Detection and Tracking for Traffic Surveillance. Majlesi Journal of Mechatronic Systems, 9(1), 17-22. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/430
Issue
Section
Articles
