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Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2009, Vol. 10 Issue (12): 1750-1758    DOI: 10.1631/jzus.A0820743
Computer Science and Technology     
Bayesian moving object detection in dynamic scenes using an adaptive foreground model
Sheng-yang YU, Fang-lin WANG, Yun-feng XUE, Jie YANG
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract  Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.

Key wordsMoving object detection      Foreground model      Kernel density estimation (KDE)      MAP-MRF estimation     
Received: 24 October 2008      Published: 21 October 2009
CLC:  TP391.41  
Cite this article:

Sheng-yang YU, Fang-lin WANG, Yun-feng XUE, Jie YANG. Bayesian moving object detection in dynamic scenes using an adaptive foreground model. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(12): 1750-1758.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A0820743     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2009/V10/I12/1750

[1] Xiang PAN, Yi-jun WU. GSM-MRF based classification approach for real-time moving object detection[J]. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(2): 250-255.