Optics and Precision Engineering, Volume. 26, Issue 10, 2584(2018)
Action recognition using geometric features and recurrent temporal attention network
[3] [3] HAN F, REILY B, HOFF W, et al.. Space-time representation of people based on 3D skeletal data [J]. Computer Vision & Image Understanding, 2017,158(C): 85-105.
[4] [4] LI Q W, XI SH Y, WANG T, et al.. Human pose estimation based on configuration constraints and trajectory optimization [J]. Opt. Precision Eng., 2017, 25(4): 528-537.(in Chinese)
[5] [5] RAHMANI H, MAHMOOD A, HUYNH D Q, et al.. Real time action recognition using histograms of depth gradients and random decision forests [C]. Proceedings of IEEE Winter Conference on Applications of Computer Vision, 2014: 626-633.
[6] [6] YANG X, TIAN Y L. Effective 3D action recognition using EigenJoints [J]. Journal of Visual Communication & Image Representation, 2014, 25(1): 2-11.
[7] [7] BOUBOU S, SUZUKI E. Classifying actions based on histogram of oriented velocity vectors [J]. Journal of Intelligent Information Systems, 2015, 44(1): 49-65.
[8] [8] ZHANG S, LIU X, XIAO J. On geometric features for skeleton-based action recognition using multilayer lstm networks [C]. Proceedings of IEEE Winter Conference on Applications of Computer Vision, Los Alamitos: IEEE Computer Society Press, 2017: 148-157.
[9] [9] DU Y, WANG W, WANG L. Hierarchical recurrent neural network for skeleton based action recognition [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, USA: IEEE Press, 2015: 1110-1118.
[10] [10] ZHU W, LAN C, XING J, et al. Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks [C]. AAAI Conference on Artificial Intelligence, Palo Alto, USA: AAAI Press, 2016: 3697-3703.
[11] [11] LIU J, SHAHROUDY A, XU D, et al.. Spatio-temporal LSTM with trust gates for 3D human action recognition [C]. Proceedings of the European Conference on Computer Vision, Heidelberg: Springer, 2016: 816-833.
[12] [12] SHARMA S, KIROS R, SALAKHU R. Action Recognition using Visual Attention [C]. Proceedings of the International Conference on Learning Representations, 2016: 1-11.
[13] [13] LEE I, KIM D, KANG S, et al.. Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks [C]. Proceedings of the IEEE International Conference on Computer Vision, Los Alamitos: IEEE Computer Society Press, 2017: 1012-1020.
[14] [14] KAR A, RAI N, SIKKA K, et al.. Adascan: Adaptive scan pooling in deep convolutional neural networks for human action recognition in videos [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2017: 3376-3385.
[15] [15] SONG S, LAN C, XING J, et al.. An end-to-end spatio-temporal attention model for human action Recognition from Skeleton Data [C]. AAAI Conference on Artificial Intelligence, Palo Alto, USA: AAAI Press, 2017: 4263-4270.
[16] [16] LI W, ZHANG Z, LIU Z. Action recognition based on a bag of 3d points [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2010: 9-14.
[17] [17] XIA L, CHEN C C, AGGARWAL J K. View invariant human action recognition using histograms of 3D joints [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2012: 20-27.
[18] [18] VEMULAPALLI R, ARRATE F, CHELLAPPA R. Human action recognition by representing 3D skeletons as points in a Lie group.[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2014: 588-595.
[19] [19] RAHMANI H, MAHMOOD A, HUYNH D, et al.. Histogram of oriented principal components for cross-view action recognition [J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(12): 2430-2443.
[20] [20] RAHMANI H, MAHMOOD A, HUYNH D, et al.. HOPC: Histogram of oriented principal components of 3D point louds for action recognition [C]. European Conference on Computer Vision, 2014: 742-757.
[21] [21] WANG J, LIU Z, WU Y, et al.. Learning actionlet ensemble for 3D human action recognition [J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(5): 914-927.
[22] [22] RAHMANI H, MIAN A. 3d action recognition from novel viewpoints [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos: IEEE Computer Society Press, 2016: 1506-1515.
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LI Qing-hui, LI Ai-hua, ZHENG Yong, FANG Hao. Action recognition using geometric features and recurrent temporal attention network[J]. Optics and Precision Engineering, 2018, 26(10): 2584
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Received: Feb. 6, 2018
Accepted: --
Published Online: Dec. 26, 2018
The Author Email: Qing-hui LI (mailto; brightlishi@gmail.comlqhuiu1212@126.com)