• Infrared and Laser Engineering
  • Vol. 47, Issue 2, 203007 (2018)
Pei Xiaomin1、2、*, Fan Huijie2, and Tang Yandong2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3788/irla201847.0203007 Cite this Article
    Pei Xiaomin, Fan Huijie, Tang Yandong. Action recognition method of spatio-temporal feature fusion deep learning network[J]. Infrared and Laser Engineering, 2018, 47(2): 203007 Copy Citation Text show less

    Abstract

    Action recognition from natural scene was affected by complex illumination conditions and cluttered backgrounds. There was a growing interest in solving these problems by using 3D skeleton data. Firstly, considering the spatio-temporal features of human actions, a spatio-temporal fusion deep learning network for action recognition was proposed; Secondly, view angle invariant character was constructed based on geometric features of the skeletons. Local spatial character was extracted by short-time CNN networks. A spatio-LSTM network was used to learn the relation between joints of a skeleton frame. Temporal LSTM was used to learn spatio-temporal relation between skeleton sequences. Lastly, NTU RGB+D datasets were used to evaluate this network, the network proposed achieved the state-of-the-art performance for 3D human action analysis. Experimental results show that this network has strong robustness for view invariant sequences.
    Pei Xiaomin, Fan Huijie, Tang Yandong. Action recognition method of spatio-temporal feature fusion deep learning network[J]. Infrared and Laser Engineering, 2018, 47(2): 203007
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