Handling complex events in surveillance tasks

Abstract

In this paper we fully develop a fall detection application that focuses on complex event detection. We use a decoupled approach, whereby the definition of events and of their complexity is fully detached from low and intermediate image processing level. We focus on context independence and flexibility to allow the reuse of existing approaches on recognition task. We build on existing proposals based on domain knowledge representation through ontologies. We encode knowledge at the rule level, thus providing a more flexible way to handle complexity of events involving more actors and rich time relationships. We obtained positive results from an experimental dataset of 22 recordings, including simple and complex fall events. © 2011 Springer-Verlag.