Search search
Publication Date
to
Vol. 65
Latest Volume
All Volumes
PIERM 139 PIERM 138 PIERM 137 PIERM 136 PIERM 135 PIERM 134 PIERM 133 PIERM 132 PIERM 131 PIERM 130 PIERM 129 PIERM 128 PIERM 127 PIERM 126 PIERM 125 PIERM 124 PIERM 123 PIERM 122 PIERM 121 PIERM 120 PIERM 119 PIERM 118 PIERM 117 PIERM 116 PIERM 115 PIERM 114 PIERM 113 PIERM 112 PIERM 111 PIERM 110 PIERM 109 PIERM 108 PIERM 107 PIERM 106 PIERM 105 PIERM 104 PIERM 103 PIERM 102 PIERM 101 PIERM 100 PIERM 99 PIERM 98 PIERM 97 PIERM 96 PIERM 95 PIERM 94 PIERM 93 PIERM 92 PIERM 91 PIERM 90 PIERM 89 PIERM 88 PIERM 87 PIERM 86 PIERM 85 PIERM 84 PIERM 83 PIERM 82 PIERM 81 PIERM 80 PIERM 79 PIERM 78 PIERM 77 PIERM 76 PIERM 75 PIERM 74 PIERM 73 PIERM 72 PIERM 71 PIERM 70 PIERM 69 PIERM 68 PIERM 67 PIERM 66 PIERM 65 PIERM 64 PIERM 63 PIERM 62 PIERM 61 PIERM 60 PIERM 59 PIERM 58 PIERM 57 PIERM 56 PIERM 55 PIERM 54 PIERM 53 PIERM 52 PIERM 51 PIERM 50 PIERM 49 PIERM 48 PIERM 47 PIERM 46 PIERM 45 PIERM 44 PIERM 43 PIERM 42 PIERM 41 PIERM 40 PIERM 39 PIERM 38 PIERM 37 PIERM 36 PIERM 35 PIERM 34 PIERM 33 PIERM 32 PIERM 31 PIERM 30 PIERM 29 PIERM 28 PIERM 27 PIERM 26 PIERM 25 PIERM 24 PIERM 23 PIERM 22 PIERM 21 PIERM 20 PIERM 19 PIERM 18 PIERM 17 PIERM 16 PIERM 14 PIERM 13 PIERM 12 PIERM 11 PIERM 10 PIERM 9 PIERM 8 PIERM 7 PIERM 6 PIERM 5 PIERM 4 PIERM 3 PIERM 2 PIERM 1
All Journals
PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2018-03-05
Visual Image Sequential Motion Detection via Half Quadratic Minimization Method
By
Ran Zhu,

    Dr. Ran Zhu

    College of Electronic Science and Engineering

    National University of Defense Technology

    China

    Homepage

Yunli Long,

    Dr. Yunli Long

    College of Electronic Science and Engineering

    National University of Defense Technology

    China

    Homepage

Wei An

    Dr. Wei An

    College of Electronic Science and Engineering

    National University of Defense Technology

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 65, 101-109, 2018
doi:10.2528/PIERM17112801 | Google Scholar

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.
Download Full Article (787) View Full Article volume
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, 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        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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.        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

28. Tikhonov, A. N. and V.Y. Arsenin, Solutions of Ill-posed Problems, Winston and Sons, 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        Google Scholar

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.        Google Scholar

Download Full Article (787) View Full Article volume


The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.