Video Sequences Foreground Enhancement Using Hidden Markov Model

Abstract:

Foreground detection is an important part in video surveillance system. The detection results will significantly affect the performance of tracking, abnormal behavior analysis and other following procedures. Many algorithms have been proposed to improve the detection performance. However, these algorithms simply focus on one single frame, ignoring the relationship among the detection results of one target in successive frames. This paper presents a novel foreground enhancement algorithm using Hidden Markov Model (HMM). In a video sequence, one target in successive frames usually has similar shape, size, et al. With this property, the target can be modeled by HMM and enhanced using the result of its prior frame. The observation of HMM is obtained by ViBe. The enhancement result is then estimated by using Maximum A Posteriori (MAP). Experimental results show that compared with the state-of-art algorithm, the proposed method can enhance foreground detection effectively.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 3872-3876
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.3872
• Online since: July 2014
• Authors: Dong Yue Lv*, Dong Yan Liu, Zhi Pei Huang, Neng Hai Yu, Jian Kang Wu
• Keywords: Foreground Enhancement, Hidden Markov Model (HMM)

References

[1] L. Patino, H. Benhadda, N. Nefzi, et al. Abnormal behavior detection in video protection systems. International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011), Sophia Antipolis : France, (2011).

[2] O. Otto, J. L. Gornall, G. Stober, et al. High-speed video-based tracking of optically trapped colloids. Journal of Optics, 13(2011)4, 40-51.

DOI: 10.1088/2040-8978/13/4/044011

[3] Q. Luo, X. Kong, G. Zeng, et al. Human action detection via boosted local motion histograms. Machine Vision and Applications, 21(2010)3, 377-389.

DOI: 10.1007/s00138-008-0168-5

[4] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, et al. Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(2010)9, 1627-1645.

DOI: 10.1109/tpami.2009.167

[5] H. Zhou, Y. Yuan, C. Shi, Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding, 113(2009)3, 345-352.

DOI: 10.1016/j.cviu.2008.08.006

[6] C. R. Wren, A. Azarbayejani, T. Darrell, et al. Pfinder: Real-time tracking of the human body. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(1997)7, 780-785.

DOI: 10.1109/34.598236

[7] A. M. McIvor. Background subtraction techniques. Proc. of Image and Vision Computing, 1(2000)3, 155-163.

[8] Z. Zivkovic. Improved adaptive Gaussian mixture model for background subtraction. Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2004, 2, 28-31.

DOI: 10.1109/icpr.2004.1333992

[9] M. Piccardi. Background subtraction techniques: a review. Systems, Man and Cybernetics, 2004 IEEE International Conference on, 2004, 4, 3099-3104.

DOI: 10.1109/icsmc.2004.1400815

[10] Y. L. Tian, M. Lu, A. Hampapur. Robust and efficient foreground analysis for real-time video surveillance. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, 1, 1182-1187.

DOI: 10.1109/cvpr.2005.304

[11] A. Elgammal, D. Harwood, L. Davis. Non-parametric model for background subtraction. Computer Vision—ECCV 2000, 2000, 751-767.

DOI: 10.1007/3-540-45053-x_48

[12] A. Elgammal, R. Duraiswami, D. Harwood, et al. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 90(2002)7, 1151-1163.

DOI: 10.1109/jproc.2002.801448

[13] L. Maddalena, A. Petrosino. A self-organizing approach to background subtraction for visual surveillance applications. Image Processing, IEEE Transactions on, 17(2008)7, 1168-1177.

DOI: 10.1109/tip.2008.924285

[14] O. Javed, K. Shafique, M. Shah. A hierarchical approach to robust background subtraction using color and gradient information. Motion and Video Computing, 2002. Proceedings. Workshop on, 2002, 22-27.

DOI: 10.1109/motion.2002.1182209

[15] O. Barnich, M. Van Droogenbroeck. ViBe: a powerful random technique to estimate the background in video sequences. Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, Taipei, 2009, 945-948.

DOI: 10.1109/icassp.2009.4959741

[16] O. Barnich, M. Van Droogenbroeck. ViBe: A universal background subtraction algorithm for video sequences. Image Processing, IEEE Transactions on, 20(2011)6, 1709-1724.

DOI: 10.1109/tip.2010.2101613