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- W1574757072 abstract "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.KeywordsJoint AngleGradient DescentParticle FilterMotion CaptureBody ModelThese 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." @default.
- W1574757072 created "2016-06-24" @default.
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- W1574757072 date "2007-11-14" @default.
- W1574757072 modified "2023-09-26" @default.
- W1574757072 title "Gradient-Enhanced Particle Filter for Vision-Based Motion Capture" @default.
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- W1574757072 doi "https://doi.org/10.1007/978-3-540-75703-0_3" @default.
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