基于运动方向的异常行为检测

Anomaly Detection Based on Motion Direction

提出了一种基于运动方向的异常行为检测方法.根据不同行为的运动方向具有不同的规律性,该方法采用块运动方向描述不同的动作,并利用支持向量机(Support vector machine,SVM)对实时监控视频进行异常行为检测.为了减少噪声运动的影响,同时有效保留小幅度运动的前景目标,在行为描述之前,本文采用了背景边缘模型对每一视频帧进行前景帧(有目标出现的视频帧)判断.在行为描述时,先提取相应视频段的所有前景帧的块运动方向,然后对这些运动方向进行归一化直方图统计得到该视频段的行为特征.在走廊等公共场景中的实验结果表明,该方法能够对单人以及多人的复杂行为进行有效检测,对运动过程中目标大小的变化、光照的变化以及噪声等具有较好的鲁棒性,而且计算复杂度小,能够实现实时监控.

A novel algorithm is proposed in this paper to detect anomalous human behaviors based on motion directions. According to different motion direction rules for different events, we introduce block-based motion directions to model those events, and use support vector machine （SVM） to detect the abnormalous actions from real-time monitoring video sequences. To increase the robustness against noise and to capture the slight movement of the object, we select the foreground frames （the frames having human object） with a background edge model before the action feature extraction. Then, action features are extracted using normalized histogram analysis from the motion directions of all the foreground frames. Our experiments on public areas such as hallway show that our algorithm is able to track complex actions of single and multiple people accurately and is robust against the variation of object size, lighting, and noise during their movements. Our algorithm is of low computation complexity thus it can be used for real time monitoring.