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

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Faion, Florian
Format: Buch
Sprache:eng
Schlagworte:
T1-995
Technology, Engineering, Agriculture, Industrial processes
Technology: general issues
Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Faion, Florian
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.
doi_str_mv 10.5445/KSP/1000054248
format Book
fullrecord <record><control><sourceid>oapen</sourceid><recordid>TN_cdi_oapen_doabooks_61100</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>61100</sourcerecordid><originalsourceid>FETCH-LOGICAL-c8408-318b8e236c3d19fdd9a91caed6b24a02c1311c88816018799dd76e71ed7f02483</originalsourceid><addsrcrecordid>eNotkE1LAzEURQMqWGu3btzkD4zNm8zkY1lKtWKxhY7grmSSN5p2TIZmRPvvHdG7uZvDgXsJuQF2VxZFOX3abqbAhpRFXqgzcsUlh5KVIOU5GYESMuMK-CWZpLQfsFxpXYp8RF6ro7EHH97o4rvH4NDRdb1H2yfqA32OPp3oJvrQ03kbP12iX75_p7Oua701vY_hF6uwxe6ICYNFuj2lHj_SNbloTJtw8t9j8nK_qObLbLV-eJzPVplVBVMZB1UrzLmw3IFunNNGgzXoRJ0XhuUWOIBVSoFgoKTWzkmBEtDJhg1T-Zjc_nmj6TDsXDR1jIe0EzD8wX8AfzRRbg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>book</recordtype></control><display><type>book</type><title>Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems</title><source>DOAB: Directory of Open Access Books</source><creator>Faion, Florian</creator><creatorcontrib>Faion, Florian</creatorcontrib><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.</description><identifier>ISSN: 1867-3813</identifier><identifier>ISBN: 3731505177</identifier><identifier>ISBN: 9783731505174</identifier><identifier>DOI: 10.5445/KSP/1000054248</identifier><language>eng</language><publisher>KIT Scientific Publishing</publisher><subject>T1-995 ; Technology, Engineering, Agriculture, Industrial processes ; Technology: general issues ; Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood</subject><creationdate>2016</creationdate><tpages>XV, 197</tpages><format>XV, 197</format><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>306,776,780,782,27902,55285</link.rule.ids></links><search><creatorcontrib>Faion, Florian</creatorcontrib><title>Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems</title><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.</description><subject>T1-995</subject><subject>Technology, Engineering, Agriculture, Industrial processes</subject><subject>Technology: general issues</subject><subject>Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood</subject><issn>1867-3813</issn><isbn>3731505177</isbn><isbn>9783731505174</isbn><fulltext>true</fulltext><rsrctype>book</rsrctype><creationdate>2016</creationdate><recordtype>book</recordtype><sourceid>V1H</sourceid><recordid>eNotkE1LAzEURQMqWGu3btzkD4zNm8zkY1lKtWKxhY7grmSSN5p2TIZmRPvvHdG7uZvDgXsJuQF2VxZFOX3abqbAhpRFXqgzcsUlh5KVIOU5GYESMuMK-CWZpLQfsFxpXYp8RF6ro7EHH97o4rvH4NDRdb1H2yfqA32OPp3oJvrQ03kbP12iX75_p7Oua701vY_hF6uwxe6ICYNFuj2lHj_SNbloTJtw8t9j8nK_qObLbLV-eJzPVplVBVMZB1UrzLmw3IFunNNGgzXoRJ0XhuUWOIBVSoFgoKTWzkmBEtDJhg1T-Zjc_nmj6TDsXDR1jIe0EzD8wX8AfzRRbg</recordid><startdate>2016</startdate><enddate>2016</enddate><creator>Faion, Florian</creator><general>KIT Scientific Publishing</general><scope>V1H</scope></search><sort><creationdate>2016</creationdate><title>Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems</title><author>Faion, Florian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c8408-318b8e236c3d19fdd9a91caed6b24a02c1311c88816018799dd76e71ed7f02483</frbrgroupid><rsrctype>books</rsrctype><prefilter>books</prefilter><language>eng</language><creationdate>2016</creationdate><topic>T1-995</topic><topic>Technology, Engineering, Agriculture, Industrial processes</topic><topic>Technology: general issues</topic><topic>Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood</topic><toplevel>online_resources</toplevel><creatorcontrib>Faion, Florian</creatorcontrib><collection>DOAB: Directory of Open Access Books</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Faion, Florian</au><format>book</format><genre>book</genre><ristype>BOOK</ristype><btitle>Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems</btitle><seriestitle>Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory</seriestitle><date>2016</date><risdate>2016</risdate><issn>1867-3813</issn><isbn>3731505177</isbn><isbn>9783731505174</isbn><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.</abstract><pub>KIT Scientific Publishing</pub><doi>10.5445/KSP/1000054248</doi><tpages>XV, 197</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1867-3813
ispartof
issn 1867-3813
language eng
recordid cdi_oapen_doabooks_61100
source DOAB: Directory of Open Access Books
subjects T1-995
Technology, Engineering, Agriculture, Industrial processes
Technology: general issues
Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood
title Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T08%3A42%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oapen&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=book&rft.btitle=Tracking%20Extended%20Objects%20in%20Noisy%20Point%20Clouds%20with%20Application%20in%20Telepresence%20Systems&rft.au=Faion,%20Florian&rft.date=2016&rft.issn=1867-3813&rft.isbn=3731505177&rft.isbn_list=9783731505174&rft_id=info:doi/10.5445/KSP/1000054248&rft_dat=%3Coapen%3E61100%3C/oapen%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true