A Bayesian Framework for Online Interaction Classification

Abstract

Real-time automatic human behavior recognition is oneof the most challenging tasks for intelligent surveillancesystems. Its importance lies in the possibility of robust detectionof suspicious behaviors in order to prevent possiblethreats. The widespread integration of tracking algorithmsinto modern surveillance systems makes it possible to acquiredescriptive motion patterns of different human activities.In this work, a statistical framework for human interactionrecognition based on Dynamic Bayesian Networks(DBNs) is presented: the environment is partitioned by atopological algorithm into a set of zones that are used to definethe state of the DBNs. Interactive and non-interactivebehaviors are described in terms of sequences of significantmotion 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 detectand cope with potential threats in real-time.