A Bayesian Framework for Online Interaction Classification

Real-time automatic human behavior recognition is one of the most challenging tasks for intelligent surveillance systems. Its importance lies in the possibility of robust detection of suspicious behaviors in order to prevent possible threats. The widespread integration of tracking algorithms into modern surveillance systems makes it possible to acquire descriptive motion patterns of different human activities. In this work, a statistical framework for human interaction recognition based on Dynamic Bayesian Networks (DBNs) is presented: the environment is partitioned by a topological algorithm into a set of zones that are used to define the state of the DBNs. Interactive and non-interactive behaviors are described in terms of sequences of significant motion events in the topological map of the environment. Finally, by means of an incremental classification measure, a scenario can be classified while it is currently evolving. In this way an autonomous surveillance system can detect and cope with potential threats in real-time.