基于模糊理论的行人异常动作检测

为在智能监控系统中自动识别行人的异常动作,提出简化的人体关节模型图。根据行人躯干和四肢轮廓角度的变化,设计用于模糊化的函数式。提出利用躯干和四肢的模糊隶属度通过计算来得到整个人异常度的一种基于模糊理论异常行为判别的算法。在系统实现中,提出利用质心轨迹和模糊判别的联合方法来甄别行人是否异常的方法。模糊判别可实现在视频监控范围内对行人行为的主动分析,从而能够对行人异常的动作做出识别并进行报警处理。通过实验证明该方法具有较高的识别率。

Abnormal Behavior of Pedestrian Detection Based on Fuzzy Theory

To automatically identify pedestrian abnormal movement in Intelligent Monitoring System, a simplified articulated model of human body is presented. A fuzzification function using the variety in trunk and limbs contour angles of pedestrian is designed. Then an abnormal behavior discrimination algorithm based on fuzzy theory is proposed. The algorithm applies fuzzy membership of the trunk and limbs of pedestrian to get the overall degree of anomaly. In the system realization, a method of combining center of mass trajectory and fuzzy discriminant is proposed to discriminate the anomaly of pedestrian. Fuzzy discriminant can implement active analysis of pedestrian behavior in visual surveillance and thereby detect irregularities to recognize abnormal behavior and alarm. The experimental results show that the proposed algorithm has a higher recognition rate.