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Tracking humans using prior and learned representations of shape and appearance

Tracking a moving person is challenging because a person's appearance in images changes significantly due to articulation, viewpoint changes, and lighting variation across a scene. And different people appear differently due to numerous factors such as body shape, clothing, skin color, and hair. In this paper, we introduce a multi-cue tracking technique that uses prior information about the 2D image shape of people in general along with an appearance model that is learned online for a specific individual. Assuming a static camera, the background is modeled and updated online. Rather than performing thresholding and blob detection during tracking, a foreground probability map (FPM) is computed which indicates the likelihood that a pixel is not the projection of the background. Offline, a shape model of walking people is estimated from the FPMs computed from training sequences. During tracking, this generic prior model of human shape is used for person detection and to initialize a tracking process. As this prior model is very generic, a model of an individual's appearance is learned online during the tracking. As the person is tracked through a sequence using both shape and appearance, the appearance model is refined and multi-cue tracking becomes more robust.