基于积分通道特征的异常行为检测算法

对视频中的行人异常行为检测问题进行了研究。针对传统行人异常行为检测算法在准确性和兼容性方面的不足,提出一种基于积分通道特征的异常行为检测算法;该算法利用背景分割和行人信息统计的方式,对不同背景下的视频模型进行了建模。结合对行人个体的轨迹分析,对运动个体的位置进行异常行为检测。算法首先对检测区域采取区域划分,然后采用改进的积分通道特征行人检测算法对目标进行检测,最后采取Mean-shift算法对目标进行跟踪。最后的实验数据表明该算法整体性能有所提高。

Abnormal behavior detection based on integral channel feature algorithm

This paper focuses on the pedestrian abnormal behavior detection in video. With regard to the shortcomings of the traditional pedestrian abnormal behavior algorithm, such as, the error detection and compatibility, this paper proposes a new algorithm. It uses the method of background segmentation and pedestrian information statistics, and the modeling poses video modular under different background, so it detects abnormal behavior of the position by analysis trajectory of the pedestrian. The algorithm proposed in this paper firstly use regional division method, then it detects target by optimized integral channel feature algorithm, lastly, the algorithm use mean-shift method to track target. The experiment results suggests that the overall performance of the algorithm proposed in this paper can be improved.