| Video segmentation using Maximum Entropy Model |
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| 作者姓名： | 秦莉娟 庄越挺 潘云鹤 吴飞 |
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| 作者单位： | School of Computer Science, Zhejiang University, Hangzhou 310027, China |
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| 基金项目： | 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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| 摘 要： | 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 ofcodewords 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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| 关 键 词： | 视频分割 最大熵模型 层分割 视频监控 目标检测 |
| 收稿时间： | 2005-01-25 |
| 修稿时间： | 2005-06-22 |
Video segmentation using Maximum Entropy Model |
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| Qin LiJuan;Zhuang YueTing;Pan YunHe;Wu Fei.Video segmentation using Maximum Entropy Model[J].Journal of Zhejiang University Science,2005,6(B08):47-52. |
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| Authors: | Qin LiJuan;Zhuang YueTing;Pan YunHe;Wu Fei |
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| Abstract: | |
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| Keywords: | Layers segmentation Maximum Entropy Model Visual surveillance |
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