DocumentCode :
2826923
Title :
Joint pose estimation and action recognition in image graphs
Author :
Raja, Kumar ; Laptev, Ivan ; Pérez, Patrick ; Oisel, Lionel
Author_Institution :
Technicolor Res. & Innovation, Cesson-Sévigné, France
fYear :
2011
fDate :
11-14 Sept. 2011
Firstpage :
25
Lastpage :
28
Abstract :
Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.
Keywords :
gesture recognition; graph theory; pose estimation; KTH dataset; action recognition; data driven approach; discriminatively trained graphical model; human analysis; human appearance subspace; human pose estimation; image graphs; Conferences; Estimation; Graphical models; Humans; Image recognition; Joints; Training; Action Recognition in still images; Graph optimization; Pose estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
ISSN :
1522-4880
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2011.6116197
Filename :
6116197
Link To Document :
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