Human pose estimation; Orientation; Machine learning
Abstract :
[en] In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we first estimate the orientation of the standing person seen by the camera and then use this information to dynamically select a pose estimation model suited for this particular orientation. We evaluated our method on a public dataset of realistic depth images with precise ground truth joints location. Our experiments show that our method decreases the error of a state-of-the-art pose estimation method by 30%, or reduces the size of the needed learning set by a factor larger than 10.
Research center :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège Telim
Disciplines :
Computer science
Author, co-author :
Azrour, Samir ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Pierard, Sébastien ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Geurts, Pierre ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Van Droogenbroeck, Marc ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
A two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically
Publication date :
September 2017
Event name :
Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)
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