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Tracking Discontinuous Motion Using Bayesian Inference

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 1843)

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.

Keywords

  • Bayesian Inference
  • Hand Position
  • Gesture Recognition
  • Belief Revision
  • Hand Orientation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2000 Springer-Verlag Berlin Heidelberg

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive

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