Automatic Acquisition of Context Models and its Application to Video Surveillance

This paper addresses the problem of automatically acquiring context models from data. Context and human behavior are represented using a state model, called situation model. This model consists of different layers referring to entities, filters, roles, relations, situation and situation relationship. We propose a framework for the automatic acquisition of these different layers. In particular, this paper proposes a novel generic situation acquisition algorithm. The algorithm is also successfully applied to a video surveillance task and is evaluated by the public CAVIAR video database. The results are encouraging.