Foreground detection is a fundamental step of extracting information in many visual surveillance applications, but background edge pixels are mistakenly identified as foreground pixels, which reduces the foreground detection accuracy. So a foreground detection algorithm based on motion information is proposed in this paper. Firstly, motion information of the candidate foreground pixel in the binary image is analyzed and a nonparametric model of background motion information distribution is constructed. Then, the likelihood probability between motion information of the candidate foreground pixel and the model is calculated. And the real foreground is determined by an adaptive threshold, which is estimated utilizing Mean-shift and information entropy. By using the adaptive threshold, the approach can overcome defects of using only one global threshold. Finally, according to the detected foreground and background motion information, the background model is updated by using a first-in first-out manner. The experimental results demonstrate that the proposed algorithm is suitable and effective for foreground detection in camera jitter scenes.
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