A Context Model and Reasoning System to improve object trackingin complex scenarios

Tracking algorithms in computer vision usually fail when dealing with complex scenarios. This paper presents an extension of a general tracking system that uses context knowledge to solve tracking issues. The context layer represents knowledge about the context of the analyzed scenario and applies rules to reason with it, in order to assess the general tracking layer at different stages and enhance tracking results. The context knowledge representation and the reasoning methods are general and can be easily adapted to different scenarios. The experimentation results show that the performance of the tracking system is considerably improved, while the efficiency requirements that are mandatory in real-time systems are satisfied.