College of Computer Science and Information Engineering Zhejiang Gongshang University, Hangzhou 310018, China

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Abstract—This paper presents a novel MoG based method for foreground detection and segmentation in video surveillance. Normal MoG is different to deal with the foreground objects that stay in the scene for a long time and segment difficult foreground objects from one blob. We improve MoG by adopting posterior feedback information of Kalman filter tracking, to robustly modeling the background and to perfect the foreground segmentation result. Experiments and comparisons show that our method is robust and accurate in video surveillance.

Index Terms—Video surveillance, Mixture of Gaussian, posterior information, Kalman filter tracking.

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Cite: Xun Wang, Jie Sun, Hao-Yu Peng, " Foreground Object Detecting Algorithm based on Mixture of Gaussian and Kalman Filter in Video Surveillance," Journal of Computers vol. 8, no. 3, pp. 693-700, 2013.