基于光流的ATM机异常行为实时检测

Real-Time Detection of ATM Abnormal Events Based on Optical Flow

异常行为检测在自助银行智能监控系统领域中有广泛的应用前景.本文针对此应用领域,提出了基于区域光流特征的异常行为检测方法.首先利用混合高斯模型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运动前景;采用lucas-kanade光流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,计算区域内直方图的运动熵发现候选异常区域,再利用支持向量机进行分类.从实验结果可以看出,能够较好的识别出异常事件,并且实时性较好,能够满足实际应用需求.

Abnormal behavior detection has a wide application prospect in the field of self-service banking intelligent monitoring system. In this paper, an anomaly detection method based on regional optical flow feature is proposed. Firstly,the mixed Gaussian model is used to represent the change of the background pixels and the background model is updated.The motion foreground is extracted from the video sequence with the background difference method. The optical flow information in the moving region is calculated with the lucas-kanade optical flow method. The weight-oriented histogram is used to describe the behavior, and the motion anomaly region of the histogram is calculated by using the motion entropy of the histogram. Then the SVM is used to classify the anomaly regions. From the experimental results, it can be seen that the abnormal events can be identified better and the real-time performance is better, which can meet the practical application requirements.