Abstract
Robustly tracking people in visual scenes is an important task for surveillance, human-computer interfaces and visually mediated interaction. Existing attempts at tracking a person’s head and hands deal with ambiguity, uncertainty and noise by intrinsically assuming a consistently continuous visual stream and/or exploiting depth information. We present a method for tracking the head and hands of a human subject from a single view with no constraints on the continuity of motion. Hence the tracker is appropriate for real-time applications in which the availability of visual data is constrained, and motion is discontinuous. Rather than relying on spatio-temporal continuity and complex 3D models of the human body, a Bayesian Belief Network deduces the body part positions by fusing colour, motion and coarse intensity measurements with contextual semantics.
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Sherrah, J., Gong, S. (2000). Tracking Discontinuous Motion Using Bayesian Inference. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_10
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DOI: https://doi.org/10.1007/3-540-45053-X_10
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