GuARD: A Real-Time System for Detecting Aggressive Human Behavior in Cage Environment

Abstract

The relative closeness in a cage environment, such as lock-up or elevator, will become a place that is conducive to conduct criminal activities such as fighting. Monitoring the activities, in the cage environment, therefore, became a necessity. However, placing security guards could be inefficient and ineffective, as it is impossible to monitor the scene 24 by 7. A vision-based system, employing video analysis technology, to detect abnormalities such as aggressive behavior, becomes a challenging and emerging problem. In order to monitor suspicious activities in a cage environment, the system should be able track individuals from the scene, to identify their action, and to keep a record of how often these aggressive behaviors happen. On top of the previous consideration, the system should be implemented in real-time, whereby, the following conditions were taken into consideration, being: (1) wide angle (fish-eye) (2) resolution (low) (3) number of people (4) lighting (low). This paper proposes to develop a vision-based system that is able to monitor aggressive activities of individuals in a cage environment. This work focuses on analyzing the temporal feature of aggressive movement, taking consideration of the acquisition limitations discusses previously. Experimental results show that the proposed system is easily realized and achieved real-time performance, even in low performance computer.