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| Abnormal behavior detection based on regional optical flow |
| DU Jian-hao, XU Li |
| College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract A human abnormal behavior detecting approach was proposed based on optical flow features in the motion area in order to meet the needs of intelligent video surveillance. An improved model of mixture of Gaussians was proposed to indicate the variation of background pixels in order to increase the robustness against lighting changes and environmental disturbances and reliably extract the motion area. Then the background model was adaptively updated. Foreground was obtained from video sequences by background subtraction. The motion area was labeled as several regions of interest, and the optical flow features in each labeled region were obtained using the Lucas-Kanade algorithm. Amplitude-based weighted orientation histogram derived from the optical flow features was defined to measure the anomaly of human activity. Then the entropy of each labeled region was computed to recognize abnormal events. Experiments were conducted on various video datasets, and the results were presented to verify the effectiveness of the proposed scheme．
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Published: 01 July 2011
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基于区域光流特征的异常行为检测
为了满足智能视频监控的需求,提出一种基于区域光流特征的异常行为检测方法.为了减少光照变化、环境扰动等因素对光流的影响及可靠地提取出运动区域,采用改进的混合高斯模型(MoG)来表示背景像素的变化并自适应更新背景模型,用背景差法从视频序列中提取出运动前景.通过最近邻法对前景进行区域标记,采用Lucas-Kanade方法计算出运动区域内的光流信息.采用基于幅值的加权方向直方图描述行为,计算运动区域内直方图的熵来判断行为的异常.基于不同场景下的视频序列所进行的实验测试结果验证了所提方法的有效性.
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