Automatic aggression detection inside trains
Zhenke Yang, Léon J. M. Rothkrantz · 2010
This paper presents work addressing the challenges of video analysis for automatic detection of aggression in a train. Using data from surveillance cameras, the system assists human operators in their work. It is unobtrusive and respects the privacy of passengers. We used existing algorithms to recognize and classify human behavior. While evaluating the algorithms we paid special attention to their ability to cope with environment specific issues, such as varying lighting conditions and (self)occlusions. A passenger behavior model was developed based on many hours of observing and studying professional operators as they analyze and respond to surveillance data. Experiments were conducted in a real train to evaluate the detection system.