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Tracking Discontinuous Motion Using Bayesian Inference

  • Jamie Sherrah
  • 1]Shaogang Gong
Conference paper
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jamie Sherrah
    • 1
  • 1]Shaogang Gong
  1. 1.Department of Computer ScienceQueen Mary and Westfield CollegeLondonUK

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