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

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...

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Príomhchruthaitheoir: Faion, Florian
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Foilsithe / Cruthaithe: KIT Scientific Publishing 2021
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author Faion, Florian
author_browse Faion, Florian
author_facet Faion, Florian
author_sort Faion, Florian
collection Directory of Open Access Books
description 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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institution Directory of Open Access Books
language eng
publishDate 2021
publishDateRange 2021
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publisherStr KIT Scientific Publishing
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spelling doab-20.500.12854ir-611002024-04-09T23:16:18Z Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems Faion, Florian T1-995 Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues 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. 2021-02-12T06:13:43Z 2021-02-12T06:13:43Z 2019-07-30 20:02:01 2016 book 35413 18673813 9783731505174 https://directory.doabooks.org/handle/20.500.12854/61100 eng Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory image/jpeg Attribution-ShareAlike 4.0 International https://www.ksp.kit.edu/9783731505174 KIT Scientific Publishing 10.5445/KSP/1000054248 10.5445/KSP/1000054248 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731505174 XV, 197 p. open access
spellingShingle T1-995
Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Faion, Florian
Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
title Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
title_full Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
title_fullStr Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
title_full_unstemmed Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
title_short Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
title_sort tracking extended objects in noisy point clouds with application in telepresence systems
topic T1-995
Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
topic_facet T1-995
Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
url 35413
work_keys_str_mv AT faionflorian trackingextendedobjectsinnoisypointcloudswithapplicationintelepresencesystems