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Structural-attentioned LSTM for action recognition based on skeleton
Author(s): Pengcheng Wang; Shaobin Li
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Paper Abstract

In this paper, the Spatio-Temporal graph of Structural-RNN[6] is developed and applied to action recognition task. We proposed a Structural-Attentioned LSTM network by adding joints, changing the specific connection mode in the original spatio-temporal graph, and introducing attention mechanism to enable the network to select edges with best representation of action automatically. We take multiple experiments on the public dataset JHMDB[10] to verify the validity of our model, achieved good results when only limited features were used.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361A (29 October 2018); doi: 10.1117/12.2513868
Show Author Affiliations
Pengcheng Wang, Communication Univ. of China (China)
Shaobin Li, Communication Univ. of China (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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