A real time aggressive human behaviour detection system in cage environment across multiple cameras
Kim Meng Liang, Zulaikha Kadim, Hock Woon Hon, Phooi Yee Lau · International Journal of Computational Vision and Robotics · 2019
The monitoring of activities in the enclosed cage environments to detect abnormalities such as aggressive behaviour, employing a real-time video analysis technology, has become an emerging and challenging problem. Such system should be able: 1) to track individuals; 2) to identify their action; 3) to keep a record of how often the aggressive behaviour happened, at the scene. On top of that, the system should be implemented in real-time, whereby, the following limitations should be taken into consideration: 1) viewing angle (fish-eye); 2) low resolution; 3) number of people; 4) low lighting (normal); 5) number of cameras. This paper proposes to develop a vision-based system that is able to monitor aggressive activities of individuals in an enclosed cage environment using multiple cameras considering the above-mentioned conditions. Experimental results show that the proposed system is easily realised and achieved impressive real-time performance, even on low end computers.