Multimodal Multipart Learning for Action Recognition in Depth Videos

Multimodal Multipart Learning for Action Recognition in Depth Videos
Title:
Multimodal Multipart Learning for Action Recognition in Depth Videos
Other Titles:
IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI:
10.1109/TPAMI.2015.2505295
Publication Date:
03 December 2015
Citation:
A. Shahroudy; T. T. Ng; Q. Yang; G. Wang, "Multimodal Multipart Learning for Action Recognition in Depth Videos," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 doi: 10.1109/TPAMI.2015.2505295
Abstract:
The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors.We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
0162-8828
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