Optical Instruments, Volume. 44, Issue 4, 39(2022)

Multiscale hypergraph convolutional network for skeleton-based action recognition

Xiaofei QIN1, Ying ZHAO1, Yijie ZHANG1, Ruijie DU1, Hanwen QIAN1, Meng CHEN2, Wenqi ZHANG2, and Xuedian ZHANG1
Author Affiliations
  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 2Institute of Aerospace System Engineering of Shanghai, Shanghai 201109, China
  • show less
    References(24)

    [1] [1] GOWAYYED M A, TKI M, HUSSEIN M E, et al. Histogram of iented displacements (HOD): describing trajecties of human joints f action recognition[C]Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence. Beijing: IJCAI, 2013.

    [2] [2] VEMULAPALLI R, ARRATE F, CHELLAPPA R. Human action recognition by representing 3D skeletons as points in a lie group[C]Proceedings of 2014 IEEE Conference on Computer Vision Pattern Recognition. Columbus: IEEE, 2014: 588 – 595.

    [3] [3] DING Z W, WANG P C, OGUNBONA P O, et al. Investigation of different skeleton features f CNNbased 3D action recognition[C]Proceedings of 2017 IEEE International Conference on Multimedia & Expo Wkshops (ICMEW). Hong Kong, China: IEEE, 2017: 617 – 622.

    [4] [4] LI C, ZHONG Q Y, XIE D, et al. Skeletonbased action recognition with convolutional neural wks[C]Proceedings of 2017 IEEE International Conference on Multimedia & Expo Wkshops (ICMEW). Hong Kong, China: IEEE, 2017: 597 – 600.

    [5] [5] LI C K, WANG P C, WANG S, et al. Skeletonbased action recognition using LSTM CNN[C]Proceedings of 2017 IEEE International Conference on Multimedia & Expo Wkshops (ICMEW). Hong Kong, China: IEEE, 2017: 585 – 590.

    [6] [6] LIU J, SHAHROUDY A, XU D, et al. Spatiotempal LSTM with trust gates f 3D human action recognition[C]Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer, 2016: 816 – 833.

    [7] LIU J, WANG G, DUAN L Y, et al. Skeleton-based human action recognition with global context-aware attention LSTM networks[J]. IEEE Transactions on Image Processing, 27, 1586-1599(2018).

    [8] [8] SI C Y, CHEN W T, WANG W, et al. An attention enhanced graph convolutional LSTM wk f skeletonbased action recognition[C]Proceedings of 2019 IEEECVF Conference on Computer Vision Pattern Recognition. Long Beach: IEEE, 2019: 1227 – 1236.

    [9] [9] YAN S J, XIONG Y J, LIN D H. Spatial tempal graph convolutional wks f skeletonbased action recognition[C]Proceedings of the ThirtySecond AAAI Conference on Artificial Intelligence Thirtieth Innovative Applications of Artificial Intelligence Conference Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. New leans: AAAI, 2018.

    [10] CHEN Y X, MA G Q, YUAN C F, et al. Graph convolutional network with structure pooling and joint-wise channel attention for action recognition[J]. Pattern Recognition, 103, 107321(2020).

    [11] [11] SONG Y F, ZHANG Z, SHAN C F, et al. Stronger, faster me explainable: a graph convolutional baseline f skeletonbased action recognition[C]Proceedings of the 28th ACM International Conference on Multimedia. Virtual Event: ACM, 2020: 1625 – 1633.

    [12] [12] YE F F, PU S L, ZHONG Q Y, et al. Dynamic GCN: contextenriched topology learning f skeletonbased action recognition[C]Proceedings of the 28th ACM International Conference on Multimedia. Virtual Event: ACM, 2020: 55 – 63.

    [13] [13] SHI L, ZHANG Y F, CHENG J, et al. Twostream adaptive graph convolutional wks f skeletonbased action recognition[C]Proceedings of 2019 IEEECVF Conference on Computer Vision Pattern Recognition. Long Beach: IEEE, 2019: 12026 – 12035.

    [14] [14] LI M S, CHEN S H, CHEN X, et al. Actionalstructural graph convolutional wks f skeletonbased action recognition[C]Proceedings of 2019 IEEECVF Conference on Computer Vision Pattern Recognition. Long Beach: IEEE, 2019: 3590 – 3598.

    [15] [15] LI M S, CHEN S H, ZHAO Y H, et al. Dynamic multiscale graph neural wks f 3D skeleton based human motion prediction[C]Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition. Seattle: IEEE, 2020: 211 – 220.

    [16] YANG W J, ZHANG J L, CAI J J, et al. Shallow graph convolutional network for skeleton-based action recognition[J]. Sensors, 21, 452(2021).

    [17] [17] ZHANG P F, LAN C L, ZENG W J, et al. Semanticsguided neural wks f efficient skeletonbased human action recognition[C]Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition. Seattle: IEEE, 2020: 1109 – 1118.

    [18] HAO X K, LI J, GUO Y C, et al. Hypergraph neural network for skeleton-based action recognition[J]. IEEE Transactions on Image Processing, 30, 2263-2275(2021).

    [19] [19] LIU Z Y, ZHANG H W, CHEN Z H, et al. Disentangling unifying graph convolutions f skeletonbased action recognition[C]Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition. Seattle: IEEE, 2020: 140 – 149.

    [20] [20] SHAHROUDY A, LIU J, NG T T, et al. NTU RGB+ D: a large scale dataset f 3D human activity analysis[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition. Las Vegas: IEEE, 2016: 1010 – 1019.

    [21] [21] KAY W, CARREIRA J, SIMONYAN K, et al. The Kiics human action video dataset[Z]. arXiv: 1705.06950, 2017.

    [22] CAO Z, HIDALGO G, SIMON T, et al. OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 172-186(2021).

    [23] [23] KIM T S, REITER A. Interpretable 3D human action analysis with tempal convolutional wks[C]Proceedings of 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 1623 – 1631.

    [24] [24] SHI L, ZHANG Y F, CHENG J, et al. Skeletonbased action recognition with directed graph neural wks[C]Proceedings of 2019 IEEECVF Conference on Computer Vision Pattern Recognition. Long Beach: IEEE, 2019: 7904 – 7913.

    Tools

    Get Citation

    Copy Citation Text

    Xiaofei QIN, Ying ZHAO, Yijie ZHANG, Ruijie DU, Hanwen QIAN, Meng CHEN, Wenqi ZHANG, Xuedian ZHANG. Multiscale hypergraph convolutional network for skeleton-based action recognition[J]. Optical Instruments, 2022, 44(4): 39

    Download Citation

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

    Category: APPLICATION TECHNOLOGY

    Received: Jan. 6, 2022

    Accepted: --

    Published Online: Oct. 19, 2022

    The Author Email:

    DOI:10.3969/j.issn.1005-5630.2022.004.006

    Topics