Tracking human under occlusion based on kernel density estimation
WANG Xuan-he, LIU Ji-lin
Institute of Information and Communication Engineering, Zhejiang Provincial Key Laboratory of
Information Network Technology, Zhejiang University, Hangzhou 310027, China
The blob model, color model and motion model were built by fully using the space information, the motion information and the color information. The moving foreground targets based on mixture Gaussian model were detected. The people body in stored model was clustered to acquire the blob model based on Epanechnikov kernel density estimation algorithm. Color density function using non-parametric kernel density estimation algorithm and motion density function using Gauss distribution were obtained. The posterior probability images were attained by estimating the foreground of the current frame using the color density function and the motion density function. Then the occluded people in the posterior probability images were segmented. Finally, the results after segmenting occluded people were the final tracking targets. Experimental results show that the proposed algorithm efficiently solves the problem of tracking people under occlusion
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