A framework dealing with Uncertainty for Complex Event Recognition, in "AVSS 2010

This paper presents a constraint-based approach for video event recognition with probabilistic reasoning for handling uncertainty. The main advantage of constraint-based approaches is the possibility for human expert to model composite events with complex temporal constraints. But the approaches are usually deterministic and do not enable the convenient mechanism of probability reasoning to handle the uncertainty. The first advantage of the pro-posed approach is the ability to model and recognize com-posite events with complex temporal constraints. The sec-ond advantage is that probability theory provides a consis-tent framework for dealing with uncertain knowledge for a robust and reliable recognition of complex event. This ap-proach is evaluated with 4 real healthcare videos and a pub-lic video ETISEO’06. The results are compared with state of the art method. The comparison shows that the proposed approach improves significantly the process of recognition and characterizes the likelihood of the recognized events. 1