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A Neoteric Approach Based on Multi Task Learning Network for Skeletal 3D Action Recognition

Authors(2) :-T. Seshagiri, S. Varadarajan

This paper presents a new representation of skeleton sequences for 3D action recognition. Existing methods based on hand-crafted features or recurrent neural networks cannot adequately capture the complex spatial structures and the long term temporal dynamics of the skeleton sequences, which are very important to recognize the actions. In this paper, we propose to transform each channel of the 3D coordinates of a skeleton sequence into a clip. Each frame of the generated clip represents the temporal information of the entire skeleton sequence, and one particular spatial relationship between the skeleton joints. The entire clip incorporates multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We also propose a Multi-task Learning Network (MTLN) to learn the generated clips for action recognition. The proposed MTLN processes all the frames of the generated clips in parallel to explore the spatial and temporal information of the skeleton sequences. The proposed method has been extensively tested on challenging benchmark datasets. Experimental results consistently demonstrate the superiority of the proposed learning method for 3D action recognition compared to existing techniques.
T. Seshagiri, S. Varadarajan
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Publication Details
  Published in : Volume 4 | Issue 8 | May-June 2018
  Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 76-84
Manuscript Number : IJSRST18489
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
T. Seshagiri, S. Varadarajan, "A Neoteric Approach Based on Multi Task Learning Network for Skeletal 3D Action Recognition", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.76-84, May-June-2018.
Journal URL : http://ijsrst.com/IJSRST18489

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