1 |
罗会兰, 童康, 孔繁胜. 基于深度学习的视频中人体动作识别进展综述[J].电子学报, 2019, 47(5): 1162-1173.
|
|
LUOHui-lan, TONGKang, KONGFan-sheng.Review of human action recognition in videos based on deep learning[J]. Acta Electronica Sinica, 2019, 47(5): 1162-1173. (in Chinese)
|
2 |
YANS, XIONGY, LIND, et al. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. New Orleans: AAAI, 2018: 7444-7452.
|
3 |
SIC, CHENW, WANGW, et al. An attention enhanced graph convolutional LSTM network for skeleton-based action recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE,2019: 1227-1236.
|
4 |
SHIL, ZHANGY, CHENGJ, et al. Skeleton-based action recognition with multi-stream adaptive graph convolutional networks[J]. IEEE Transactions on Image Processing, 2020, 29: 9532-9545.
|
5 |
LEIT, ZHANGY, WANGS I, et al. Simple recurrent units for highly parallelizable recurrence[EB/OL]. (2018)[2021]. .
|
6 |
SHEQ, MUG, GANH, et al. Spatio-temporal SRU with global context-aware attention for 3D human action recognition[J]. Multimedia Tools and Applications, 2020, 79(17-18): 12349-12371.
|
7 |
PARKC, LEEC, HONGL, et al. S2-Net: Machine reading comprehension with SRU based self-matching networks[J]. ETRI Journal, 2019, 41(3): 371-382.
|
8 |
ZHUW, LANC, XINGJ, et al. Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Phoenix: AAAI, 2016: 3697-3703.
|
9 |
ZHANGL, ZHUG, MEIL, et al. Attention in convolutional LSTM for gesture recognition[C]//Proceedings of the Advances in Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018: 1953-1962.
|
10 |
Di GangiM A, FedericoM. Deep neural machine translation with weakly-recurrent units[EB/OL]. (2018)[2021]. .
|
11 |
SONGS, LANC, XINGJ, et al. An end-to-end spatio-temporal attention model for human action recognition from skeleton data[C]//Proceedings of the AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2017:4263-4270.
|
12 |
朱红蕾, 朱昶胜, 徐志刚.人体行为识别数据集研究进展[J]. 自动化学报, 2018, 44(6): 978-1004.
|
|
ZHUHong-lei, ZHUChang-sheng, XUZhi-gang. Research advances on human activity recognition datasets[J].Acta Automatica Sinica,2018,44(6):978-1004.(in Chinese)
|
13 |
XIEC, LIC, ZHANGB, et al. Memory attention networks for skeleton-based action recognition[EB/OL]. (2018)[2021]. .
|
14 |
WANGJ, NIEX, XIAY, et al. Cross-view action modeling, learning and recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 2649-2656.
|
15 |
CHENGK, ZHANGY, HEX, et al. Skeleton-based action recognition with shift graph convolutional Network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 183-192.
|
16 |
穆高原. 基于深度学习的危险驾驶行为识别研究[D]. 杭州: 杭州电子科技大学, 2020.
|
|
MUGao-yuan. Study on dangerous driving behavior recognition based on deep learning[D]. Hangzhou: Hangzhou Dianzi University, 2020. (in Chinese)
|
17 |
VemulapalliR, ArrateF, ChellappaR. 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. Columbus: IEEE, 2014: 588-595.
|
18 |
WANGJ, LIUZ, WUY, et al. Learning actionlet ensemble for 3D human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 36(5): 914-927.
|
19 |
DUY, WANGW, WANGL. Hierarchical recurrent neural network for skeleton based action recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 1110-1118.
|
20 |
LIUM, LIUH, CHENC. Enhanced skeleton visualization for view invariant human action recognition[J]. Pattern Recognition, 2017, 68: 346-362.
|
21 |
LEED Kim, KANGS, LEES. Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 1012-1020.
|
22 |
SHIL, ZHANGY, CHENGJ, et al. Two-stream adaptive graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 12026-12035.
|
23 |
SHAHROUDYA, LIUJ, NGT T, et al. Nturgb+d: A large scale dataset for 3D human activity analysis[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1010-1019.
|