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Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems

Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems

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We discuss theory and application of extended object tracking. This task is challenging as sensor noise prevents a correct association of the measurements to their sources on the object, the shape itself might be unknown a priori, and due to occlusion effects, only parts of the object are visible at a given time. We propose an approach to track the parameters of arbitrary objects, which provides new solutions to the above challenges, and marks a significant advance to the state of the art.

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Keywords

  • Technology, engineering, agriculture
  • Technology: general issues
  • Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood

Links

DOI: 10.5445/KSP/1000054248

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