Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 8, 1095(2023)

Behavior recognition based on time-dependent attention

Kuan LIU1,2, Wei WANG1,2, Hong-ting SHEN1, Hong-tao HOU1,2, Min-zhen GUO1,2, and Zi-jiang LUO1、*
Author Affiliations
  • 1School of Information, Guizhou University of Finance and Economics,Guiyang 550025, China
  • 2Intelligent Middle, Beijing Cloud Trace Technology Co., Ltd., Beijing 100089, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
    Equations (0)
    References (29)
    Tools
    Paper Information
    Topics
    References(29)

    [1] WANG H, SCHMID C. Action recognition with improved trajectories[C], 3551-3558(2013).

    [2] PENG X J, ZOU C Q, QIAO Y et al. Action recognition with stacked fisher vectors[C], 581-595(2014).

    [3] LAN Z Z, LIN M, LI X C et al. Beyond Gaussian pyramid: multi-skip feature stacking for action recognition[C], 204-212(2015).

    [4] WANG Y, TRAN V, HOAI M. Evolution-preserving dense trajectory descriptors[J/OL](2017).

    [5] ZDRAVEVSKI E, LAMESKI P, TRAJKOVIK V et al. Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering[J]. IEEE Access, 5, 5262-5280(2017).

    [6] KARPATHY A, TODERICI G, SHETTY S et al. Large-scale video classification with convolutional neural networks[C], 1725-1732(2014).

    [7] SIMONYAN K, ZISSERMAN A. Two-stream convolutional networks for action recognition in videos[C], 568-576(2014).

    [8] TRAN D, BOURDEV L, FERGUS R et al. Learning spatiotemporal features with 3D convolutional networks[C], 4489-4497(2015).

    [9] CARREIRA J, ZISSERMAN A. Quo Vadis, action recognition? A new model and the kinetics dataset[C], 4724-4733(2017).

    [10] XIE Z, ZHOU Y, WU K W et al. Activity recognition based on spatial-temporal attention LSTM[J]. Chinese Journal of Computers, 44, 261-274(2021).

    [11] ZHANG H Y, AN Z. Human action recognition based on improved two-stream spatiotemporal network[J]. Optics and Precision Engineering, 29, 420-429(2021).

    [12] PAN N, JIANG M, KONG J. Human action recognition algorithm based on spatio-temporal interactive attention model[J]. Laser & Optoelectronics Progress, 57, 181506(2020).

    [13] ZHANG W Q, WANG Z Q, ZHANG L. Human action recognition combining sequential dynamic images and two-stream convolutional network[J]. Laser & Optoelectronics Progress, 58, 0210007(2021).

    [14] CHEN Y, GONG S M. Human action recognition network based on improved channel attention mechanism[J]. Journal of Electronics & Information Technology, 43, 3538-3545(2021).

    [15] AFZA F, KHAN M A, SHARIF M et al. A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection[J]. Image and Vision Computing, 106, 104090(2021).

    [16] XU J, SONG R, WEI H L et al. A fast human action recognition network based on spatio-temporal features[J]. Neurocomputing, 441, 350-358(2021).

    [17] CHEN Y X, ZHANG Z Q, YUAN C F et al. Channel-wise topology refinement graph convolution for skeleton-based action recognition[C], 13339-13348(2021).

    [18] CHEN Z, LI S C, YANG B et al. Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition[C], 1113-1122(2021).

    [19] PLIZZARI C, CANNICI M, MATTEUCCI M. Spatial temporal transformer network for skeleton-based action recognition[C], 694-701(2021).

    [20] LI Q, DENG Y H, WANG J. Campus violence action recognition based on lightweight graph convolution network[J]. Chinese Journal of Liquid Crystals and Displays, 37, 530-538(222).

    [21] FEICHTENHOFER C, FAN H Q, MALIK J et al. SlowFast networks for video recognition[C], 6201-6210(2019).

    [22] DONG M, FANG Z L, LI Y F et al. AR3D: attention residual 3D network for human action recognition[J]. Sensors, 21, 1656(2021).

    [23] HU J, SHEN L, ALBANIE S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [24] SOOMRO K, ZAMIR A R, SHAH M. UCF101: a dataset of 101 human actions classes from videos in the wild[J/OL](2012).

    [25] KUEHNE H, JHUANG H, GARROTE E et al. HMDB: a large video database for human motion recognition[C], 2556-2563(2011).

    [26] ZHU Y, LAN Z Z, NEWSAM S et al. Hidden two-stream convolutional networks for action recognition[C], 363-378(2018).

    [27] HUANG M, QIAN H M, HAN Y et al. R(2+1)D-based two-stream CNN for human activities recognition in videos[C], 7932-7937(2021).

    [28] CHEN L, LIU Y G, MAN Y C. Spatial-temporal channel-wise attention network for action recognition[J]. Multimedia Tools and Applications, 80, 21789-21808(2021).

    [29] LI J P, WEI P, ZHENG N N. Nesting spatiotemporal attention networks for action recognition[J]. Neurocomputing, 459, 338-348(2021).

    Tools

    Get Citation

    Copy Citation Text

    Kuan LIU, Wei WANG, Hong-ting SHEN, Hong-tao HOU, Min-zhen GUO, Zi-jiang LUO. Behavior recognition based on time-dependent attention[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(8): 1095

    Download Citation

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

    Category: Research Articles

    Received: Oct. 11, 2022

    Accepted: --

    Published Online: Oct. 9, 2023

    The Author Email: Zi-jiang LUO (luozijiang@mail.gufe.edu.cn)

    DOI:10.37188/CJLCD.2022-0330

    Topics