Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87094
Title: Multimodal multipart learning for action recognition in depth videos
Authors: Shahroudy, Amir
Ng, Tian-Tsong
Yang, Qingxiong
Wang, Gang
Keywords: Action Recognition
Kinect
Issue Date: 2016
Source: Shahroudy, A., Ng, T.-T., Yang, Q., & Wang, G. (2016). Multimodal multipart learning for action recognition in depth videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2123-2129.
Series/Report no.: IEEE Transactions on Pattern Analysis and Machine Intelligence
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.
URI: https://hdl.handle.net/10356/87094
http://hdl.handle.net/10220/45224
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2015.2505295
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TPAMI.2015.2505295].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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