The use of context can be a very relevant cue for computer vision-based systems, in order to eliminate a lot of ambiguity and uncertainty, otherwise inherent, in the human-computer interaction. Despite of the fact that its obvious importance is widely acknowledged, the great majority of the systems nowdays still lack of this capability. In this paper, we propose faces as a primary contextual information for person detection and present face tracking as a basic procedure for context-driven focus of attention applications. Our proposal was implemented in a combined system, by integrating a motion-based approach (Particle Filter) and a model-based approach (Ada-Boost).
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