Detection of Unattended and Removed Objects in Public Areas

Abstract:

This paper presents a novel approach to detect unattended and removed objects from a single fixed camera video for surveillance applications. In this paper two backgrounds rather than any tracking information are employed to detect static foregrounds. Each of the backgrounds is modeled by three Gaussian mixtures. We make a subtraction between two foregrounds extracted by the background subtraction method to initially determine the static regions. Then the region-level density analysis and texture information are combined to remove false static foreground regions caused by illumination changes and objects that are in the static-moving transitions. Finally, classification of the unattended and removed is determined by a method that compares color histograms of the static foreground zone and its extended areas in current frame. Experimental results show that our method can work well in simple scenario as well as complex environments with an unsmooth background.

• Periodical: Applied Mechanics and Materials (Volume 678)
• Pages: 166-173
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.678.166
• Online since: October 2014
• Authors: Xiao Huang, Yong Zhao*, Zhong Xin Cheng, Yu Le Yuan, Xiao Hui Liang
• Keywords: Background Subtraction, Mixture Of Gaussians, Removed Object, Static Foreground, Unattended Object

References

[1] Smith, K., Quelhas, P., & Gatica-Perez, D. (2006, June). Detecting abandoned luggage items in a public space. In Proceedings of the 9th IEEE International Workshop on Performance Evaluation in Tracking and Surveillance (PETS'06) (pp.75-82).

[2] Auvinet, E., Grossmann, E., Rougier, C., Dahmane, M., & Meunier, J. (2006, June). Left-luggage detection using homographies and simple heuristics. InProc. 9th IEEE International Workshop on Performance Evaluation in Tracking and Surveillance (PETS'06) (pp.51-58.

[3] Tian, Y. L., Lu, M., & Hampapur, A. (2005, June). Robust and efficient foreground analysis for real-time video surveillance. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp.1182-1187.

DOI: 10.1109/cvpr.2005.304

[4] Porikli, F. (2007, September). Detection of temporarily static regions by processing video at different frame rates. In Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on (pp.236-241). IEEE.

DOI: 10.1109/avss.2007.4425316

[5] Piccardi, M. (2004, October). Background subtraction techniques: a review. In Systems, man and cybernetics, 2004 IEEE international conference on(Vol. 4, pp.3099-3104). IEEE.

DOI: 10.1109/icsmc.2004.1400815

[6] Stauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. (Vol. 2). IEEE.

DOI: 10.1109/cvpr.1999.784637

[7] Suzuki, K., Horiba, I., & Sugie, N. (2003). Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 89(1), 1-23.

DOI: 10.1016/s1077-3142(02)00030-9

[8] Guo, Z., Zhang, L., & Zhang, D. (2010). Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern recognition, 43(3), 706-719.

DOI: 10.1016/j.patcog.2009.08.017