Journal of Terahertz Science and Electronic Information Technology , Volume. 21, Issue 5, 661(2023)
Group activity recognition based on attention mechanism and spatio-temporal information
[3] [3] SHU T,TODOROVIC S,ZHU S C. CERN:confidence-energy recurrent network for group activity recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu,HI,USA:IEEE, 2017:5523-5531.
[4] [4] TANG J, SHU X, YAN R, et al. Coherence constrained graph LSTM for group activity recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022,44(2):636-647.
[5] [5] DENG Z, VAHDAT A, HU H, et al. Structure inference machines: recurrent neural networks for analyzing relations in group activity recognition[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas,NV,USA: IEEE, 2016:4772-4781.
[6] [6] HU G,CUI B,HE Y,et al. Progressive relation learning for group activity recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Seattle,WA,USA:IEEE, 2020:980-989.
[7] [7] ZHANG Q L,YANG Y B. Sa-net:shuffle attention for deep convolutional neural networks[C]// 2021—2021 IEEE International Conference on Acoustics,Speech and Signal Processing. Toronto,ON,Canada:IEEE, 2021:2235-2239.
[9] [9] YAN R,TANG J,SHU X,et al. Participation-contributed temporal dynamic model for group activity recognition[C]// Proceedings of the 26th ACM International Conference on Multimedia. New York,NY,USA:ACM, 2018:1292-1300.
[10] [10] ZACH C,POCK T,BISCHOF H. A duality based approach for realtime Tv-L1 optical flow[C]// Proceedings of the 29th DAGM Conference on Pattern Recognition. Berlin,Heidelberg:IEEE, 2007:214-223.
[11] [11] SZEGEDY C,VANHOUCKE V,IOFFE S,et al. Rethinking the inception architecture for computer vision[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,USA:IEEE, 2016:2818-2826.
[12] [12] WU Y,HE K. Group normalization[C]// Proceedings of the European Conference on Computer Vision. Munich,MUC,Germany:Springer, 2018:3-19.
[13] [13] SHU X, ZHANG L, SUN Y, et al. Host-parasite: graph LSTM-in-LSTM for group activity recognition[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,32(2):663-674.
[14] [14] WANG X, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City,UT,USA:IEEE, 2018:7794-7803.
[15] [15] YAN R,XIE L,TANG J,et al. HiGCIN:hierarchical graph-based cross inference network for group activity recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020,(99):1.
[16] [16] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,USA:IEEE, 2016:770-778.
[17] [17] GAVRILYUK K,SANFORD R,JAVAN M,et al. Actor-transformers for group activity recognition[C]// International Conference on Computer Vision and Pattern Recognition. Seattle,WA,USA:IEEE, 2020:839-848.
[18] [18] KINGMA D P, BA J. Adam:a method for stochastic optimization[EB/OL]. [2021-01-10]. https://arxiv.org/pdf/1412.6980.pdf.
[19] [19] LI W, WEI Y, LYU S, et al. Simultaneous multi-person tracking and activity recognition based on cohesive cluster search[J].Computer Vision and Image Understanding, 2022(214):103301.
[20] [20] LI B,SHU X,YAN R. Storyboard relational model for group activity recognition[C]// Proceedings of the 2nd ACM International Conference on Multimedia in Asia. New York,NY,USA:ACM, 2021:1-7.
[21] [21] XU D,FU H,WU L,et al. Group activity recognition by using effective multiple modality relation representation with temporal-spatial attention[J]. IEEE Access, 2020(8):65689-65698.
[22] [22] IBRAHIM M S, MURALIDHARAN S, DENG Z, et al. A hierarchical deep temporal model for group activity recognition[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas,NV,USA:IEEE, 2016:1971-1980.
[23] [23] LI X,CHOO Chuah M. Sbgar:semantics based group activity recognition[C]// Proceedings of the IEEE International Conference on Computer Vision. Venice,Italy:IEEE, 2017:2876-2885.
[24] [24] QI M, QIN J, LI A, et al. stagNet: an attentive semantic RNN for group activity recognition[C]// Proceedings of the European Conference on Computer Vision. Munich,MUC,Germany:Springer, 2018:101-117.
[25] [25] PEI D,LI A,WANG Y. Group activity recognition by exploiting position distribution and appearance relation[C]// International Conference on Multimedia Modeling. Springer,Berlin:[s.n.], 2021:123-135.
[26] [26] WU J, WANG L, WANG L, et al. Learning actor relation graphs for group activity recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach,CA,USA:IEEE, 2019:9964-9974.
[27] [27] DUAN Y,WANG J. Learning key actors and their interactions for group activity recognition[C]// Chinese Conference on Pattern Recognition and Computer Vision. Berlin:Springer, 2021:53-65.
[28] [28] AZAR S M,ATIGH M G,NICKABADI A,et al. Convolutional relational machine for group activity recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach,CA,USA:IEEE, 2019:7892-7901.
[29] [29] YUAN H, NI D. Learning visual context for group activity recognition[C]// Proceedings of the 2021 AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press, 2021,35(4):3261-3269.
[30] [30] SAQLAIN M,KIM D,CHA J,et al. 3DMesh-GAR:3D human body mesh-based method for group activity recognition[J]. Sensors, 2022,22(4):1464.
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JIANGXue, QING Linbo, HUANGJianglan, LIU Bo. Group activity recognition based on attention mechanism and spatio-temporal information[J]. Journal of Terahertz Science and Electronic Information Technology , 2023, 21(5): 661
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Received: Jul. 16, 2022
Accepted: --
Published Online: Jan. 17, 2024
The Author Email: JIANGXue (1015475773@qq.com)