Visual behavior analysis tool for consumer video surveillance

Consumers video surveillance systems are now being used not only for security reasons but also for better understanding consumer behaviors. In this paper, we propose a new visual behavior analysis tool for consumer video surveillance systems. This tool can be embedded in consumer videos to automatically detect and analyze unusual events. The proposed tool is developed by using a special type of Gamma Markov chain for background modeling and Petri Nets for object classification. We present some experimental results to show the effectiveness of the proposed system which will be leading to new visual behavior analysis tools for the consumers.