Infrared and Laser Engineering, Volume. 47, Issue 2, 203007(2018)

Action recognition method of spatio-temporal feature fusion deep learning network

Pei Xiaomin1,2、*, Fan Huijie2, and Tang Yandong2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    References(14)

    [1] [1] Wang Jiang, Liu Zicheng. Mining actionlet ensemble for action recognition with depth cameras[C]//IEEE Conference on Computer Vision and Pattern Recognition,2012: 1290-1297.

    [2] [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] [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] [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.

    [5] [5] Du Yong, Wang Wei, Wang Liang. Hierarchical recurrent neural network for skeleton based action recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1110-1118.

    [6] [6] Vivek Veeriah, Naifan Zhuang. Differential recurrent neural networks for action recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015: 4041-4049.

    [7] [7] Zhu Wentao, Lan Cuiling. Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks[C]//AAAI, 2016: 3697-3704.

    [8] [8] Amir Shahroudy, Liu Jun. NTU RGB+D: A large scale dataset for 3D human activity analysis[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1010-1019.

    [9] [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] [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.

    [11] [11] Huang Zhiwu, Wan Chengde. Deep learning on Lie groups for skeleton-based action recognitio[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1243-1252.

    [12] [12] Zhou Feiyan, Jin Linpeng, Dong Jun. Review of convolutional neural networks[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1250. (in Chinese)

    [13] [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)

    [14] [14] Shao Chunyan, Ding Qinghai, Luo Haibo, et al. Target tracking using high-dimension data clustering[J]. Infrared and Laser Engineering, 2016, 45(4): 0428002. (in Chinese)

    CLP Journals

    [1] Liu Tianci, Shi Zelin, Liu Yunpeng, Zhang Yingdi. Geometry deep network image-set recognition method based on Grassmann manifolds[J]. Infrared and Laser Engineering, 2018, 47(7): 703002

    [2] Guo Qiang, Lu Xiaohong, Xie Yinghong, Sun Peng. Efficient visual target tracking algorithm based on deep spectral convolutional neural networks[J]. Infrared and Laser Engineering, 2018, 47(6): 626005

    [3] MA Shi-wei, LIU Li-na, FU Qi, WEN Jia-rui. Using PHOG fusion features and multi-class Adaboost classifier for human behavior recognition[J]. Optics and Precision Engineering, 2018, 26(11): 2827

    Tools

    Get Citation

    Copy Citation Text

    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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: 特约专栏—“深度学习及其应用”

    Received: Aug. 10, 2017

    Accepted: Oct. 28, 2017

    Published Online: Apr. 26, 2018

    The Author Email: Xiaomin Pei (pxm_neu@126.com)

    DOI:10.3788/irla201847.0203007

    Topics