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基于模糊集理论的运动目标检测 被引量:2

Moving object detection by fuzzy set theory
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摘要 针对复杂自然环境下运动目标检测中噪声多、目标检测不完整等问题,提出一种改进的基于模糊集理论的解决方法。使用金字塔多分辨率模型进行背景差分获取初步的前景掩膜,将当前帧的颜色、时间、空间、位置4种特征用模糊集表示,形成模糊向量集合簇,用模糊数学理论结合这4个向量的特征,得到每个像素点对前景的隶属度,从而检测运动目标。该方法不仅能有效地检测较完整运动目标,也可以克服自然环境下微小运动背景的影响。实验结果显示:该方法前景的识别率为0.717 4,错误率为0.011 8,能适应自然环境下动态背景的影响。 According to the problems ot numerous noises and incomplete detection of moving ob- ject detection in complex natural environment, an improved solution method based on fuzzy set theory is put forward. First, pyramid-style multi-resolution model is used for background difference, and the preliminary foreground mask is obtained. Then the model of color, time, space and locality for the current image are represented by fuzzy sets to form a cluster of fuzzy vector set. At last, the features of these four vectors are fused together by fuzzy mathematic theory to get the membership of foreground for each pixel and detect the object. The method not only can detect relative complete moving object effectively, but overcome the influence of small moving background in natural environment at the same time. The experiments are carried out and the results show that the foreground recognition rate by the proposed method is 0. 7174 and the error rate is 0. 011 8. It can adapt to the influence of the dynamic background in natural environment.
出处 《应用光学》 CAS CSCD 北大核心 2013年第5期820-824,共5页 Journal of Applied Optics
基金 973计划(2012CB725301) 中央高校基本科研业务费专项资金(T201221207)
关键词 运动目标检测 模糊集 复杂自然背景 多分辨率 moving object detection fuzzy set complex natural background multi-resolution
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参考文献11

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