Chinese Journal of Liquid Crystals and Displays, Volume. 37, Issue 4, 530(2022)

Campus violence action recognition based on lightweight graph convolution network

Qi LI1, Yao-hui DENG2、*, and Jiao WANG2
Author Affiliations
  • 1School of Electrical Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021,China
  • 2School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China
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    References(15)

    [7] YAN S J, XIONG Y J, LIN D H. Spatial temporal graph convolutional networks for skeleton-based action recognition[C], 7444-7452(2018).

    [8] ZHAO M F, LIU S, SONG T et al. Spatial enhancement of human skeleton behavior recognition based on residual independent recurrent neural network[J]. Laser Journal, 41, 37-43(2020).

    [9] CAO Y, LIU C, HUANG Z L et al. Skeleton-based action recognition based on spatio-temporal adaptive graph convolutional neural-network[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 48, 5-10(2020).

    [11] KONG W, LIU Y, LI H et al. A survey of action recognition methods based on graph convolutional network[J]. Control and Decision, 36, 1537-1546(2021).

    [13] ZHU J B, XU Z L, SUN Y W et al. Detection of dangerous behaviors in power stations based on OpenPose multi-person attitude recognition[J]. Automation & Instrumentation, 35, 47-51(2020).

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    Qi LI, Yao-hui DENG, Jiao WANG. Campus violence action recognition based on lightweight graph convolution network[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(4): 530

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    Paper Information

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    Received: Aug. 31, 2021

    Accepted: --

    Published Online: Jun. 20, 2022

    The Author Email: Yao-hui DENG (173743077@qq.com)

    DOI:10.37188/CJLCD.2021-0229

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