Elliptic fit of objects in two and three dimensions by moment of inertia optimization
Pattern Recognition Letters
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Representing and recognizing complex events in surveillance applications
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Modelling and Managing Domain Context for Automatic Surveillance Systems
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A survey on vision-based human action recognition
Image and Vision Computing
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
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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.