Classification-driven video analytics for critical infrastructure protection

Abstract

At critical infrastructure sites, either large number of onsite personnel, or many cameras are needed to keep all key access points under continuous observation. With the proliferation of inexpensive high quality video imaging devices, and improving internet bandwidth, the deployment of large numbers of cameras monitored from a central location have become a practical solution. Monitoring a high number of critical infrastructure sites may cause the operator of the surveillance system to become distracted from the many video feeds, possibly missing key events, such as suspicious individuals approaching a door or leaving an object behind. An automated monitoring system for these types of events within a video feed alleviates some of the burden placed on the operator, thereby increasing the overall reliability and performance of the system, as well as providing archival capability for future investigations. In this work, a solution that uses a background subtraction-based segmentation method to determine objects within the scene is proposed. An artificial neural network classifier is then employed to determine the class of each object detected in every frame. This classification is then temporally filtered using Bayesian inference in order to minimize the effect of occasional misclassifications. Based on the object’s classification and spatio-temporal properties, the behavior is then determined. If the object is considered of interest, feedback is provided to the background subtraction segmentation technique for background fading prevention reasons. Furthermore any undesirable behavior will generate an alert, to spur operator action.