• 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
    References

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    [2] Luvizon D C, Tabia H. Learning features combination for human action recognition from skeleton sequences[J]. Pattern Recognition Letters, 2017, 99(11): 13-20.

    [3] Ji XiaoPeng, Cheng Jun. The spatial laplacian and temporal energy pyramid representation for human action recognition using depth sequence[J]. Knowledge-Based System, 2017, 122: 64-74.

    [4] Zhang Pengfei, Lan Cuiling. View adaptive recurrent neural networks for high performance human action recognition from skeleton data[C]//ICCV 2017. International Conference on Computer Vision, 2017: 2136-2145.

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    [9] Liu Jun, Amir Shahroudy. Spatio-temporal LSTM with trust gates for 3D human action recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016.

    [10] Liu Jun, Wang Gang. Skeleton based human action recognition with global context-aware attention LSTM networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3671-3680.

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    [13] Luo Haibo, Xu Lingyun, Hui Bin, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5): 0502002. (in Chinese)

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    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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