Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
Abstract
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.
Keywords
Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial LikelihoodISBN
9783731505174Publisher
KIT Scientific PublishingPublisher website
http://www.ksp.kit.edu/Publication date and place
2016Series
Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory,Classification
Technology: general issues


