Abstract
Detecting objects of interest from a video sequence is a fundamental and critical task in automated visual surveillance. Most current approaches only focus on discriminating moving objects by background subtraction whether or not the objects of interest can be moving or stationary. In this paper, we propose layers segmentation to detect both moving and stationary target objects from surveillance video. We extend the Maximum Entropy (ME) statistical model to segment layers with features, which are collected by constructing a codebook with a set of codewords for each pixel. We also indicate how the training models are used for the discrimination of target objects in surveillance video. Our experimental results are presented in terms of the success rate and the segmenting precision.
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Project supported by the National Natural Science Foundation of China (No. 60272031), and Technology Plan Program of Zhejiang Province (No. 2003C21010), and Zhejiang Provincial Natural Science Foundation of China (No. M603202)
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Qin, Lj., Zhuang, Yt., Pan, Yh. et al. Video segmentation using Maximum Entropy Model. J. Zheijang Univ.-Sci. 6 (Suppl 1), 47–52 (2005). https://doi.org/10.1631/jzus.2005.AS0047
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DOI: https://doi.org/10.1631/jzus.2005.AS0047