Abnormal behavior detection using hybrid agents in crowded scenes

In this paper, we propose a hybrid agent method to detect abnormal behaviors in a crowded scene. In real-life situations, abnormal behavior occurs by violent movement which is apparent as sudden speeding up, or chaotic movement in a restricted area, or movement contrasting with that of one's neighbors such as in a panic situation. In our model, we categorize the behaviors of people into individual behavior and group interactive behavior. Individual behavior is defined only by native motion information such as speed and direction. By contrast, group interactive behavior is defined by information concerning interactive motion between neighbors. We propose a hybrid agent system that includes static and dynamic agents to observe efficiently the corresponding individual and interactive behaviors in a crowded scene. The static agent is assigned to a specific spot and analyzes motion information near that spot. Unlike the static agent, the dynamic agent is assigned to a moving object and analyzes motion information of neighbors as well as oneself by following the object's movement. We represent the behavior of a crowd as a bag of words through the integration of static and dynamic agent information to determine abnormalities in the crowd behavior. The experimental results show that our proposed method efficiently detects abnormal behaviors in crowded scenes. (C) 2013 Elsevier B.V. All rights reserved.