一种新的异常行为检测算法

摘要

Automatic recognition of human behavior is an important but difficult problem in the area of computer vision. In this paper, a novel approach is introduced to handle the problem. Human body is detected using the contour information and the posture features are extracted by the contour fitting. A behavior classifier based on the nortnat behavior template is established to determine whether a human behavior is normal or not. Experimental results show that this system can run in real-time for the detection of abnormal behaviors with limited information and produce robust results by making full use of posture features information.%行人异常行为的自动检测与识别是计算机视觉领域的重点和难点,同时也是智能监控系统中研究的热点问题.针对这一问题,提出了一种基于人体形态特征的异常检测算法.利用轮廓信息将目标从视频序列中分割出来,再对分割出来的目标进行轮廓拟合,根据所得到的拟合信息提取文中所定义的形态特征因子,将特征因子经过行为分类器的判定,从而决策出该行为是否异常.实验结果表明该方法实现简单,具有较好的实时性与鲁棒性,可以作为实时监控系统中异常行为检测的有效方法.