Study Of Video-Based Interior Abnormal Behavior Analysis

Vision-based human behavior analysis is currently one of the most active research fields, and to understand and analyze the human behavior is a challenging research topic. Human behavior analysis has many promising applications such as intelligent surveillance, perceptual interface and content-based video retrieval. Most research on human behavior analysis is based on single person and simple action; however, there is less research about single potential abnormal behavior and multi-person fighting behavior. As the basic of the human behavior analysis, the result of human behavior analysis always gets poor effect because of the noise in motion detection, such as varying luminance. In this dissertation, an approach to analyzing person states based on multi-viewing-angle was proposed, and then, a method to analyzing fighting behavior based on energy and to detect smoking based on color model were also proposed.Firstly, the moving person was detected using self-adaptive background updating algorithm and scene was estimated by the means of connected regions. After searching the centroid of upper part of the body, its periodic feature was extracted and the behavior was analyzed in single person scene.Secondly, human motion was discriminated by R transform in scene where there was no occlusion, and then fighting behavior was analyzed based on energy image in multi-person scene.Finally, in a given scene, person was detected by detecting skin area. And smoking was detected and analyzed by varying luminance, color model and directional information.The experimental results show that the human speed and walking states method is reasonably robust in varying luminance and shadow, and the accuracy of walking states is 92%. Moving energy can be used to recognize abnormal behavior, and the accuracy is 84% and 80% in single person and multi-person scene respectively. Smoking behaviors can be exactly analysis by multi features.