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Enhanced skeleton visualization for view invariant human action recognition
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
A survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356
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Research works printed but not linked: 19
- Manresa, 2005, Hand tracking and gesture recognition for human–computer interaction, ELCVIA Electron. Lett. Comput. Vis. Image Anal., 5, 96, 10.5565/rev/elcvia.109 · DOI · Paper page
- Baxter, 2015, Human behaviour recognition in data-scarce domains, Pattern Recognit., 48, 2377, 10.1016/j.patcog.2015.02.019 · DOI
- Chen, 2016, A novel hierarchical framework for human action recognition, Pattern Recognit., 55, 148, 10.1016/j.patcog.2016.01.020 · DOI
- Yi, 2016, Human action recognition with graph-based multiple-instance learning, Pattern Recognit., 53, 148, 10.1016/j.patcog.2015.11.022 · DOI
- Chen, 2015, Improving human action recognition using fusion of depth camera and inertial sensors, IEEE Trans. Human-Mach. Syst., 45, 51, 10.1109/THMS.2014.2362520 · DOI
- Liu, 2016, Depth context: a new descriptor for human activity recognition by using sole depth sequences, Neurocomputing, 175, 747, 10.1016/j.neucom.2015.11.005 · DOI
- Chen, 2016, A real-time human action recognition system using depth and inertial sensor fusion, IEEE Sens. J., 16, 773, 10.1109/JSEN.2015.2487358 · DOI · Paper page
- Zhang, 2012, Microsoft kinect sensor and its effect, IEEE Multimed., 19, 4, 10.1109/MMUL.2012.24 · DOI · Paper page
- Shotton, 2013, Real-time human pose recognition in parts from single depth images, Commun. ACM, 56, 116, 10.1145/2398356.2398381 · DOI · Paper page
- Ding, 2015, Multilayer joint gait-pose manifolds for human gait motion modeling, IEEE Trans. Cybern., 45, 2413, 10.1109/TCYB.2014.2373393 · DOI
- Jiang, 2015, Informative joints based human action recognition using skeleton contexts, Signal Process. Image Commun., 33, 29, 10.1016/j.image.2015.02.004 · DOI
- Junejo, 2011, View-independent action recognition from temporal self-similarities, IEEE Trans. Pattern Anal. Mach. Intell., 33, 172, 10.1109/TPAMI.2010.68 · DOI · Paper page
- Hsu, 2016, Online view-invariant human action recognition using RGB-D spatio-temporal matrix, Pattern Recognit., 60, 215, 10.1016/j.patcog.2016.05.010 · DOI
- Ofli, 2014, Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition, J. Vis. Commun. Image Represent., 25, 24, 10.1016/j.jvcir.2013.04.007 · DOI · Paper page
- Yang, 2014, Effective 3D action recognition using eigenjoints, J. Vis. Commun. Image Represent., 25, 2, 10.1016/j.jvcir.2013.03.001 · DOI · Paper page
- Borland, 2007, Rainbow color map (still) considered harmful., IEEE Comput. Gr. Appl., 27, 14, 10.1109/MCG.2007.323435 · DOI · Paper page
- Rahmani, 2016, Histogram of oriented principal components for cross-view action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 38, 2430, 10.1109/TPAMI.2016.2533389 · DOI · Paper page
- Yang, 2016, Super normal vector for human activity recognition with depth cameras, IEEE Trans. Pattern Anal. Mach. Intell., PP
- Wang, 2014, Learning actionlet ensemble for 3D human action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 36, 914, 10.1109/TPAMI.2013.198 · DOI · Paper page
Cited by
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00572 · Cited by 972
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00132 · Cited by 917
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2896631 · Cited by 523
Skeleton-based action recognition via spatial and temporal transformer networks
Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Cheng, K., Zhang, Y., Cao, C., Shi, L., Cheng, J., & Lu, H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_32 · Cited by 384
Metadata
- Authors
- Liu, M., Liu, H., & Chen, C.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 68
- Pages
- 346-362
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2593146028
- Cited by
- 842
- References
- 2
- FWCI
- 31.34
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- National Natural Science Foundation of China (61340046, 61673030, 61672079, U1613209)
- Natural Science Foundation of Guangdong Province (2015A030311034)
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- The paper
- Liu, M., Liu, H., & Chen, C. (2017). Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognition. https://doi.org/10.1016/j.patcog.2017.02.030
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
