DocumentCode :
3673980
Title :
Mixture of parts revisited: Expressive part interactions for Pose Estimation
Author :
Anoop R Katti;Anurag Mittal
Author_Institution :
IIT Madras, Chennai, India
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
59
Lastpage :
67
Abstract :
Part-based models with restrictive tree-structured interactions for the Human Pose Estimation problem, leave many part interactions unhandled. Two of the most common and strong manifestations of such unhandled interactions are self-occlusion among the parts and the confusion in the localization of the non-adjacent symmetric parts. By handling the self-occlusion in a data efficient manner, we improve the performance of the basic Mixture of Parts model by a large margin, especially on difficult poses. We address the confusion in the symmetric limb localization using a combination of two complementing trees, showing an improvement in the performance on all the parts with a very small trade-off in the running time. Finally, we show that the combination of the two solutions improves the results. We compare our HOG-based method with other methods using similar features and report results equivalent to the best method on two standard datasets with a large reduction in the running time.
Keywords :
"Training","Elbow","Head","Kinematics","Legged locomotion","Computational modeling"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE Conference on
Electronic_ISBN :
2160-7516
Type :
conf
DOI :
10.1109/CVPRW.2015.7301355
Filename :
7301355
Link To Document :
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