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This paper introduces a Surveillance Video Analysis System, called SVAS, for surveillance domain, in which the semantic rules and the definition of event models can be learned or defined by the user for automatic detection and inference of complex video events. In the scope of SVAS, an event model method named Interval-Based Spatio-Temporal Model (IBSTM) is proposed. SVAS can learn action models and...
This paper presents a framework for detecting complex events in surveillance videos. Moving objects in the foreground are detected in the object detection component of the system. Whether these foregrounds are human or not is decided in the object recognition component. Then each detected object is tracked and labeled in the object tracking component, in which true labeling of objects in the occlusion...
An event model learning framework is proposed for indoor and outdoor surveillance applications in order to decrease human intervention in the modeling process. The resulting framework makes event detection and recognition flexible, domain and scene independent. A set of predicate types is introduced which define basic spatio-temporal relations and interactions between objects and people in the videos...
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