Open Access
Issue |
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
|
|
---|---|---|
Article Number | 03014 | |
Number of page(s) | 6 | |
Section | Computing | |
DOI | https://doi.org/10.1051/itmconf/20214003014 | |
Published online | 09 August 2021 |
- Du Tran, Lubomir Bourdev, Rob Fergus,Lorenzo Torresani, Manohar Paluri “Learning Spatiotemporal Features with 3D Convolutional Networks”, IEEE International Conference on Computer Vision (ICCV), 2015. [Google Scholar]
- Tsai, Jen-Kai & Hsu, Chen-Chien & Huang, Shao-Kang. “ Deep Learning-Based Real-Time Multiple-Person Action Recognition System”, Sensors. 20. 4758. 10.3390/s20174758, Aug 2020. [Google Scholar]
- Romaissa, Beddiar & Nini, Brahim & Sabokrou, Mohammad & Hadid, Abdenour, “Vision-based human activity recognition: a survey”, Multimedia Tools and Applications. 79. 10.1007/s11042-020-09004-3, Aug 2020. [Google Scholar]
- Y. Du, W. Wang, and L. Wang, “Hierarchical recurrent neural network for skeleton based action recognition”, In IEEE Conference Paper on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1110–1118. [Google Scholar]
- S. Song, C. Lan, J. Xing, W. Zeng, “An End-to-End SpatioTemporal Attention Model for Human Action Recognition from Skeleton Data” in AAAI, pp. 4263–4270, 2017. [Google Scholar]
- Riddhi Sonkar, Sadhana Rathod, Renuka Jadhav Deepali Patil et al. “Crowd Abnormal Behaviour Detection using Deep Learning”, ITM Web of Conferences, 2020 [Google Scholar]
- T. S. Kim and A. Reiter, “Interpretable 3d human action analysis with temporal convolutional networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10.1109/CVPRW.2017.207 [Google Scholar]
- M. Niepert, M. Ahmed, Konstantin Kutzkov et al. “Learning convolutional neural networks for graph,” in International Conference on Machine Learning (ICML), 2016. [Google Scholar]
- J. Bruna, W. Zaremba, Arthur Szlam, Yann LeCun et al. “Spectral Networks and Locally Connected Networks on Graphs,” in International Conference on Learning Representations, 2014. [Google Scholar]
- M. Henaff, J. Bruna, Yann LeCun et al. “Deep convolutional networks on graph-structured data,” arXiv:1506.05163 [cs.LG], 2015 [Google Scholar]
- Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, Alex C. Kot, “NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019. 10.1109/TPAMI.2019.2916873 [Google Scholar]
- F. Caba Heilbron, B. Ghanem, J. C. Niebles, et al. .“A large-scale video benchmark for human activity understanding”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 961–970 [Google Scholar]
- W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev, and others, “The Kinetics Human Action Video Dataset,” arXiv:1705.06950 cs. CV, May 2017. [Google Scholar]
- Yu Cong,Yun Fu “Human Action Recognition and prediction: A survey”, Computer Vision and Pattern Recognition, Cornell University, June 2018. [Google Scholar]
- Gundong Guo, Alice Lai et al. “A survey on till image based human action recognition” west virginia university, may 2014. [Google Scholar]
- Chen Chen, Roozbeh Jafari, Nasser Kehtarnavaz et al. “A survey of depth and initial sensor fusion for human action recognition”, 2017. [Google Scholar]
- Hong Bo Zhang, Yi-Xiang Zhang, Bineng Zhong, Qing Lei, Lijie Yang, Ji-Xiang Du and Duan-Sheng Chen et al. “A Comprehensive Survey of Vision-Based Human Action Recognition Methods” feb 2019. [Google Scholar]
- Fangyu Liu, Shuaipeng Li, Liqiang Zhang, Chenghu Zhou, Rongtian Ye, Yuebin Wang, Jiwen Lu et al. “3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds” Tsinghua University. [Google Scholar]
- Amir Shahroudy, Jun Liu, Tian-Tsong Ng and Gang Wang et al. “NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis” Singapore Institute for Infocomm Research, april 2016. [Google Scholar]
- Aksasse H., Aksasse B., Ouanan M.,”Deep Convolutional Neural Networks for Human Activity Classification”,. In: Jain L., Peng SL., Alhadidi B., Pal S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham, 2020. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.