Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance
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 moving objects in digital videos. The moving detection and tracking system is developed based on optical flow estimation together with application and combination of various relevant computer vision and image processing techniques to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms in morphological operations. Also the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.
[1] J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Systems and experiment
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[2] J. Xiao, H. Cheng, H. Sawhney, C. Rao, and M. Isnardi, “Bilateral
filtering- based optical flow estimation with occlusion detection,” ECCV,
2006, pp. 211–224.
[3] T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, “High accuracy
optical flow estimation based on a theory for warping,” ECCV,2004, pp.
25–36.
[4] C. L. Zitnick, N. Jojic, and S. B. Kang, “Consistent segmentation
foroptical flow estimation,” IEEE ICCV, 2005, pp. 1308–1315.
[5] M. J. Black, “The robust estimation of multiple motions: Parametric and
piecewise-smooth flow fields,” Comput. Vis. Image Underst., vol.63, no.
1, pp. 75–104, Jan. 1996.
[6] Rakshit, S.; Anderson, C.H., "Computation of optical flow using basis
functions," Image Processing, IEEE Transactions on , vol.6, no.9,
pp.1246,1254, Sep 1997.
[7] Zhan, Wei, and Xiaolong Ji. "Algorithm Research on Moving Vehicles
Detection," Procedia Engineering, vol. 15, 2011,pp. 5483-5487.
[8] Ballard, Dana H. "Generalizing the Hough transform to detect arbitrary
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[9] Illingworth, John, and Josef Kittler. "A survey of the Hough
transform," Computer vision, graphics, and image processing, vol. 44,
no. 1, 1988, pp. 87-116.
[10] Shi, Xuejie. "Research on Moving Object Detection Based on Optical
Flow Mechanism." University of Science and Technology of
China, 2010, pp. 6-14.
[11] Qiaona Pei. “Moving Objects Detection and Tracking Technology based
Optical Flow”. North China University of Technology, 2009, pp. 11-14.
[12] Lucas B. D., Kanade T. “An Iterative Image Registration Technique with
an Application to Stereo Vision,” DARPA Image Understanding
Workshop, 1981, pp. 121–130.
[13] Silar, Z., and M. Dobrovolny. "Comparison of two optical flow
estimation methods using Matlab." Applied Electronics (AE), 2011
International Conference on. IEEE, 2011.
[14] Horn B. K. P., Schunck B. G. “Determining Optical Flow,” Artificial
Intelligence, vol. 17, 1981, pp. 185–204.
[15] Hageman, L., Young, D. “Applied Iterative Methods,” Academic Press,
New York, 1981, pp. 18-22.
[16] Eng, How-Lung, and Kai-Kuang Ma. "Noise adaptive soft-switching
median filter," IEEE Transactions on Image Processing, vol. 10, no. 2,
2001, pp. 242-251.
[17] Tsekeridou, Sofia, Constantine Kotropoulos, and Ioannis Pitas.
"Morphological signal adaptive median filter for still image and image
sequence filtering", IEEE International Symposium on Circuits and
Systems, vol. 4, 1998, pp. 21-24.
[18] Gonzalez, Rafael C., and E. Richard. Woods, “digital image
processing," Prentice Hall Press, ISBN 0-201-18075-8. 2002.
[19] Tang, Sze Ling, Zulaikha Kadim, Kim Meng Liang, and Mei Kuan Lim.
"Hybrid blob and particle filter tracking approach for robust object
tracking," Procedia Computer Science, vol. 1, no. 1, 2010, pp. 2549-
2557.
[20] Telagarapu, Prabhakar, MV Nageswara Rao, and Gulivindala Suresh. "A
novel traffic-tracking system using morphological and Blob analysis."
International Conference on In Computing, Communication and
Applications (ICCCA), 2012, pp. 1-4.
[21] Gangodkar, D.; Kumar, P.; Mittal, A., "Robust Segmentation of Moving
Vehicles Under Complex Outdoor Conditions," Intelligent
Transportation Systems, IEEE Transactions on , vol.13, no.4,
pp.1738,1752, Dec. 2012.
[1] J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Systems and experiment
performance of optical flow techniques,” Int. J. Comput. Vis., vol.12,
1, pp. 43–77, Feb. 1994.
[2] J. Xiao, H. Cheng, H. Sawhney, C. Rao, and M. Isnardi, “Bilateral
filtering- based optical flow estimation with occlusion detection,” ECCV,
2006, pp. 211–224.
[3] T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, “High accuracy
optical flow estimation based on a theory for warping,” ECCV,2004, pp.
25–36.
[4] C. L. Zitnick, N. Jojic, and S. B. Kang, “Consistent segmentation
foroptical flow estimation,” IEEE ICCV, 2005, pp. 1308–1315.
[5] M. J. Black, “The robust estimation of multiple motions: Parametric and
piecewise-smooth flow fields,” Comput. Vis. Image Underst., vol.63, no.
1, pp. 75–104, Jan. 1996.
[6] Rakshit, S.; Anderson, C.H., "Computation of optical flow using basis
functions," Image Processing, IEEE Transactions on , vol.6, no.9,
pp.1246,1254, Sep 1997.
[7] Zhan, Wei, and Xiaolong Ji. "Algorithm Research on Moving Vehicles
Detection," Procedia Engineering, vol. 15, 2011,pp. 5483-5487.
[8] Ballard, Dana H. "Generalizing the Hough transform to detect arbitrary
shapes," Pattern recognition, vol. 13, no. 2, 1981, pp. 111-122.
[9] Illingworth, John, and Josef Kittler. "A survey of the Hough
transform," Computer vision, graphics, and image processing, vol. 44,
no. 1, 1988, pp. 87-116.
[10] Shi, Xuejie. "Research on Moving Object Detection Based on Optical
Flow Mechanism." University of Science and Technology of
China, 2010, pp. 6-14.
[11] Qiaona Pei. “Moving Objects Detection and Tracking Technology based
Optical Flow”. North China University of Technology, 2009, pp. 11-14.
[12] Lucas B. D., Kanade T. “An Iterative Image Registration Technique with
an Application to Stereo Vision,” DARPA Image Understanding
Workshop, 1981, pp. 121–130.
[13] Silar, Z., and M. Dobrovolny. "Comparison of two optical flow
estimation methods using Matlab." Applied Electronics (AE), 2011
International Conference on. IEEE, 2011.
[14] Horn B. K. P., Schunck B. G. “Determining Optical Flow,” Artificial
Intelligence, vol. 17, 1981, pp. 185–204.
[15] Hageman, L., Young, D. “Applied Iterative Methods,” Academic Press,
New York, 1981, pp. 18-22.
[16] Eng, How-Lung, and Kai-Kuang Ma. "Noise adaptive soft-switching
median filter," IEEE Transactions on Image Processing, vol. 10, no. 2,
2001, pp. 242-251.
[17] Tsekeridou, Sofia, Constantine Kotropoulos, and Ioannis Pitas.
"Morphological signal adaptive median filter for still image and image
sequence filtering", IEEE International Symposium on Circuits and
Systems, vol. 4, 1998, pp. 21-24.
[18] Gonzalez, Rafael C., and E. Richard. Woods, “digital image
processing," Prentice Hall Press, ISBN 0-201-18075-8. 2002.
[19] Tang, Sze Ling, Zulaikha Kadim, Kim Meng Liang, and Mei Kuan Lim.
"Hybrid blob and particle filter tracking approach for robust object
tracking," Procedia Computer Science, vol. 1, no. 1, 2010, pp. 2549-
2557.
[20] Telagarapu, Prabhakar, MV Nageswara Rao, and Gulivindala Suresh. "A
novel traffic-tracking system using morphological and Blob analysis."
International Conference on In Computing, Communication and
Applications (ICCCA), 2012, pp. 1-4.
[21] Gangodkar, D.; Kumar, P.; Mittal, A., "Robust Segmentation of Moving
Vehicles Under Complex Outdoor Conditions," Intelligent
Transportation Systems, IEEE Transactions on , vol.13, no.4,
pp.1738,1752, Dec. 2012.
@article{"International Journal of Electrical, Electronic and Communication Sciences:65468", author = "Sepehr Aslani and Homayoun Mahdavi-Nasab", title = "Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance", 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 moving objects in digital videos. The moving detection and tracking system is developed based on optical flow estimation together with application and combination of various relevant computer vision and image processing techniques to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms in morphological operations. Also the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.
", keywords = "Optical flow estimation, moving object detection, tracking, morphological operation, blob analysis.", volume = "7", number = "9", pages = "1259-5", }
