Gradient-Enhanced Particle Filter for Vision-Based Motion Capture

  • Daniel Grest
  • Volker Krüger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4814)


Tracking of rigid and articulated objects is usually addressed within a particle filter framework or by correspondence based gradient descent methods. We combine both methods, such that (a) the correspondence based estimation gains the advantage of the particle filter and becomes able to follow multiple hypotheses while (b) the particle filter becomes able to propagate the particles in a better manner and thus gets by with a smaller number of particles. Results on noisy synthetic depth data show that the new method is able to track motion correctly where the correspondence based method fails. Further experiments with real-world stereo data underline the advantages of our coupled method.


Joint Angle Gradient Descent Particle Filter Motion Capture Body Model 
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 2007

Authors and Affiliations

  • Daniel Grest
    • 1
  • Volker Krüger
    • 1
  1. 1.Aalborg University Copenhagen, Denmark, Computer Vision and Machine Intelligence Lab 

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