Tracking discontinuous motion using Bayesian inference

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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 con-sistently 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1843, pp. 150–166). Springer Verlag. https://doi.org/10.1007/3-540-45053-x_10

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