Monocular 3D Human Pose Estimation by Classification

  • We present a novel approach to 2D and 3D human pose estimation in monocular images by building on and improving recent advances in this field. We take the full body pose as a combination of a 3D pose and a viewpoint and in this way define classes that are then learned by a classifier. Compared to part based approaches, our approach does not suffer from self–occluded body parts since such occlusions are characteristic for certain classes and thus are captured during class definition. Moreover, we significantly relax the requirements posed on training data by the fact that we do neither require labeled viewpoints nor background subtracted images, and the carried out action does not need to be cyclic. By combining an efficient classifier with efficient image features, we present a generic and fast way to estimate human poses in images and achieve comparable results to state-of-the art approaches which we demonstrate on a public benchmark.

Download full text files

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Thomas GreifGND, Debabrata Sengupta, Rainer LienhartGND
URN:urn:nbn:de:bvb:384-opus4-12375
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/1545
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2011-09)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Contributing Corporation:IIT Guwahati
Release Date:2011/07/18
Tag:pose estimation; human detection; random forests
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Multimedia und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht