Activity Recognition and Uncertain Knowledge in Video Scenes

Rim Romdhane 1 Carlos Fernando Crispim-Junior 1 Francois Bremond 1 Monique Thonnat 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Activity recognition has been a growing research topic in the last years and its application varies from auto-matic recognition of social interaction such as shaking hands, parking lot surveillance, traffic monitoring and the detection of abandoned luggage. This paper describes a probabilistic framework for uncertainty handling in a description-based event recognition approach. The proposed approach allows the flexible modeling of composite events with complex temporal constraints. It uses probability theory to provide a consistent framework for dealing with uncertain knowledge for the recognition of complex events. We validate the event recognition accuracy of the proposed algorithm on real-world videos. The experimental results show that our system can successfully recognize activities with a high recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of real-time event recognition
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Rim Romdhane, Carlos Fernando Crispim-Junior, Francois Bremond, Monique Thonnat. Activity Recognition and Uncertain Knowledge in Video Scenes. IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Aug 2013, Krakow, Poland. ⟨hal-01059602⟩

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