Behavior-driven video analytics system for critical infrastructure protection

The convergence of a security aware environment with the proliferation of inexpensive high quality video imaging devices has led to the deployment of cameras at a high number of critical infrastructure sites. As many cameras are needed to keep all key access points under continuous observation, an operator of the surveillance system may become distracted from the many video feeds, possibly missing key events, such as suspicious individuals leaving an object behind or approaching a door. By providing an automated system for monitoring these types of events within a video feed, some of the burden placed on the operator is alleviated, thereby increasing the overall reliability and performance of the monitoring system, as well as providing archival capability for future investigations. In this paper, we propose a solution that uses a background subtraction based segmentation method to determine objects within the scene. An artificial neural network classifier is then employed to determine the class of each object detected in each frame, which is then temporally filtered using Bayesian inference to minimize the effect of occasional misclassifications. The behavior of the object is then determined based on its classification and spatio-temporal properties, and if the object is considered of interest, feedback is provided to the background subtraction segmentation technique for background fading prevention reasons.