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Evaluation of CNN-based algorithms for human pose analysis of persons in red carpet scenarios

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dc.contributor.authorKowerko, Danny
dc.contributor.authorRichter, Daniel
dc.contributor.authorHeinzig, Manuel
dc.contributor.authorKahl, Stefan
dc.contributor.authorHelmert, Stefan
dc.contributor.authorBrunnett, Guido
dc.contributor.editorEibl, Maximilian
dc.contributor.editorGaedke, Martin
dc.date.accessioned2017-08-28T23:47:41Z
dc.date.available2017-08-28T23:47:41Z
dc.date.issued2017
dc.description.abstractWe evaluate two CNN-based algorithms for keypoint-based human pose analysis on two image test sets containing red carpet scenarios, one taken under controlled conditions in a TV studio environment and another more heterogeneous data set taken from FlickR without any restriction but to contain a red carpet. We focus on the pose of persons standing directly on the red carpet. A web application is presented allowing collaborative work to confirm or modify already pre-localised body keypoints given from the method presented in [Ca17]. These annotations helped to quickly define ground truth for the subsequent evaluation of several hundreds of persons standing on a red carpet. An own evaluation formalism is presented that adopts to the size of the respective keypoints. The TV studio data set includes coarsely defined body and head poses. Using the angular information, we are able to quantitatively define the optimum head pose angle range and limitations of facial keypoint determination.en
dc.identifier.doi10.18420/in2017_219
dc.identifier.isbn978-3-88579-669-5
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-275
dc.subjecthuman pose estimation
dc.subjectperson detection
dc.subjectcomputer vision
dc.subjectimage processing
dc.titleEvaluation of CNN-based algorithms for human pose analysis of persons in red carpet scenariosen
gi.citation.endPage2209
gi.citation.startPage2201
gi.conference.date25.-29. September 2017
gi.conference.locationChemnitz
gi.conference.sessiontitleDeep Learning in heterogenen Datenbeständen

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