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2018-03-05
Visual Image Sequential Motion Detection via Half Quadratic Minimization Method
By
Progress In Electromagnetics Research M, Vol. 65, 101-109, 2018
Abstract
In this paper, we present a straightforward numerical algorithm for visual image sequential motion detection based on half quadratic minimization method. To solve the optimization problem modeled for sequential motion detection, an auxiliary functional is introduced. The proposed algorithm is more efficient since the iterative computation is operate mainly on the current frame rather than the whole batch of images. As for the standard visual image sequences with RGB color representation, an intuitive way is to convert it to grayscale image to achieve an approximate motion detection with relatively low computational load. Instead, we propose an improved processing scheme for more accurate detection by utilizing the algorithm separately and then perform fusion on a higher level. Experiment results show that the proposed algorithm can successfully detect moving object in practical visual surveillance applications.
Citation
Ran Zhu, Yunli Long, and Wei An, "Visual Image Sequential Motion Detection via Half Quadratic Minimization Method," Progress In Electromagnetics Research M, Vol. 65, 101-109, 2018.
doi:10.2528/PIERM17112801
References

1. Burger, W. and M. Burge, Principles of Digital Image Processing: Fundamental Techniques, Springer, Berlin, 2009.

2. Yang, M. and G. Zhang, "Unsupervised target detection in sar images using scattering center model and mean shift clustering algorithm," Progress In Electromagnetics Research Letters, Vol. 35, 11-18, 2012.
doi:10.2528/PIERL12071109

3. Yang, M. and G. Zhang, "A dictionary-based image fusion for integration of SAR and optical images," Progress In Electromagnetics Research Letters, Vol. 49, 87-90, 2014.
doi:10.2528/PIERL14081801

4. Diao, W., X. Mao, and V. Gui, "Metrics for performance evaluation of preprocessing algorithms in infrared small target images," Progress In Electromagnetics Research, Vol. 115, 35-53, 2011.
doi:10.2528/PIER11012412

5. Zhao, B., S. Xiao, H. Lu, and J. Liu, "Point target detection in space-based infrared imaging system based on multi-direction filtering fusion," Progress In Electromagnetics Research M, Vol. 56, 145-156, 2017.
doi:10.2528/PIERM17030401

6. Lipton, A. J., H. Fujiyoshi, and R. S. Patil, "Moving target classification and tracking from real-time video," Proceedings of Proc. IEEE Workshop Applications of Computer Vision, 8-14, Princeton, NJ, USA, 1998.

7. Caspi, Y. and M. Irani, "Spatio-temporal alignment of sequences," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 11, 1409-1424, 2002.
doi:10.1109/TPAMI.2002.1046148

8. Barron, J. L., D. J. Fleet, and S. S. Beauchemin, "Performance of optical flow techniques," International Journal of Computer Vision, Vol. 12, No. 1, 43-77, 1994.
doi:10.1007/BF01420984

9. Barranco, F., J. Diaz, E. Ros, and B. D. Pino, "Visual system based on artificial retina for motion detection," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 39, No. 3, 752-762, 2009.
doi:10.1109/TSMCB.2008.2009067

10. Horn, B. K. P. and B. G. Schunck, "Determining optical flow," Artificial intelligence, Vol. 17, No. 1-3, 185-203, 1981.
doi:10.1016/0004-3702(81)90024-2

11. Lucas, B. D. and T. Kanade, "An iterative image registration technique with an application to stereo vision," Proceedings of International Joint Conference on Artificial Intelligence, 674-679, Vancouver, British Columbia, Canada, 1981.

12. Hu, W., T. Tan, L. Wang, and S. Maybank, "A survey on visual surveillance of object motion and behaviors," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 34, No. 3, 334-352, 2004.
doi:10.1109/TSMCC.2004.829274

13. Mhatre, S., S. Varma, and R. Nikhare, "Visual surveillance using absolute difference motion detection," Proceedings of IEEE International Conference on Technologies for Sustainable Development, 1-5, Mumbai, India, 2015.

14. Diao, W., X. Mao, H. Zheng, Y. Xue, and V. Gui, "Image sequence measures for automatic target tracking," Progress In Electromagnetics Research, Vol. 130, 447-472, 2012.
doi:10.2528/PIER12050810

15. Li, C. and Y. Jiang, "An effective background reconstruction method for video objects detection," Proceedings of IEEE International Conference on Networking and Distributed Computing, 161-165, Hangzhou, China, 2012.

16. Jiang, S., Z. Wei, S. Wang, Z. Zhou, and J. Zhang, "A new algorithm for background extraction under video surveillance," Proceedings of IEEE Conference Anthology, 1-4, China, 2013.

17. Hou, Z. and C. Han, "A background reconstruction algorithm based on pixel intensity classification," Journal of Software, Vol. 16, No. 9, 1568-1576, 2005.
doi:10.1360/jos161568

18. Zivkovic, Z. and F. V. D. Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction," Pattern Recognition Letters, Vol. 27, No. 7, 773-780, 2006.
doi:10.1016/j.patrec.2005.11.005

19. Kim, K., T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-time foreground-background segmentation using codebook model," Real-Time Imaging, Vol. 11, No. 3, 172-185, 2005.
doi:10.1016/j.rti.2004.12.004

20. Barnich, O. and M. Van Droogenbroeck, "ViBe: A universal background subtraction algorithm for video sequences," IEEE Transactions on Image Processing, Vol. 20, No. 6, 1709-1724, 2011.
doi:10.1109/TIP.2010.2101613

21. Aubert, G. and P. Kornprobst, Mathematical Problems in Image Processing, Partial Differential Equations and the Calculus of Cariations, Springer, Berlin, 2006.

22. Kornprobst, P., R. Deriche, and G. Aubert, "Image sequence analysis via partial differential equations," Journal of Mathematical Imaging and Vision, Vol. 11, No. 1, 5-26, 1999.
doi:10.1023/A:1008318126505

23. Francois, A. and G. Medioni, "Adaptive color background modeling for real-time segmentation of video streams," Proceedings of International Conference on Imaging Science, System and Technology, 1-6, 1999.

24. Cremers, D. and S. Soatto, "Variational space-time motion segmentation," Proceedings of IEEE International Conference on Computer Vision, 886-893, Nice, France, 2003.
doi:10.1109/ICCV.2003.1238442

25. Cremers, D. and S. Soatto, "Motion competition: A variational approach to piecewise parametric motion segmentation," International Journal of Computer Vision, Vol. 62, No. 3, 249-265, 2005.
doi:10.1007/s11263-005-4882-4

26. Aubert, G. and J. Aujol, "A variational approach to removing multiplicative noise," Siam Journal on Applied Mathematics, Vol. 68, No. 4, 925-946, 2008.
doi:10.1137/060671814

27. Bar, L., B. Berkels, M. Rumpf, and G. Sapiro, "A variational framework for simultaneous motion estimation and restoration of motion-blurred video," Proceedings of IEEE International Conference on Computer Vision, 1-8, Rio de Janeiro, Brazil, 2007.

28. Tikhonov, A. N. and V.Y. Arsenin, Solutions of Ill-posed Problems, Winston and Sons, Washington, D.C., 1977.

29. Rudin, L., S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, Vol. 60, No. 1-4, 259-268, 1992.
doi:10.1016/0167-2789(92)90242-F

30. Reza, H., B. Ngu, and B. Tuong, "Visual tracking in background subtracted image sequences via multi-bernoulli filtering," IEEE Transactions on Signal Processing, Vol. 61, No. 2, 392-397, 2012.