Context-Based Reasoning Using Ontologies to Adapt Visual Tracking in Surveillance

Classical tracking methods are often insufficient when dealing with complex scenarios. In order to solve tracking errors, innovative techniques based on the use of information about the context of the scene have been proposed. Context information ranges from precise measures computed on the pixels of the object neighborhood to high level representations of the entities and the activities of the scene. In this work, we focus on the second approach and propose an ontology-based extension of a general tracking procedure that reasons with abstract context descriptions to improve its accuracy. We describe the design of this extension and how reasoning is performed, as well as its advantages in surveillance scenarios.