A bio-inspired system model for interactive surveillance applications
Advances in computer vision and pattern recognition research are leading to video surveillance systems with improved scene analysis capabilities. However, up to now few works have handled the problem of how the system, along with a human operator, can actively cope with detected anomalous events. In this paper, on the basis of recent studies on artificial cognitive systems, a general framework is proposed for designing interactive, adaptable and intelligent surveillance systems. The aim of the system is to react to situations in a preventive way using actuators installed in the monitored environment. An application of the proposed system is introduced where a guard is supported in pursuing an intruder. The operator is first localized and tracked and then multi-modal guidance messages are communicated to him on a mobile device. Previous experience on the interaction dynamics between the two players is provided by a simulator, modeling guard and intruder behaviors, to predict near future events and decide the appropriate messages to be sent. Results on real world video sequences show the reliability of the simulated data to build up interaction models and predict near future events. Moreover, the system capability of learning relationships with the operator to establish efficient and personalized communications is verified.
personalized communications cognitive video surveillance interaction learning and modeling predictive decision