The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
SkeletonNet: Mining Deep Part Features for 3-D Action Recognition
Open accessAbstract
This letter presents SkeletonNet, a deep learning framework for skeleton-based 3-D action recognition. Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for action recognition. We first extract body-part-based features from each frame of the skeleton sequence. Compared to the original coordinates of the skeleton joints, the proposed features are translation, rotation, and scale invariant. To learn robust temporal information, instead of treating the features of all frames as a time series, we transform the features into images and feed them to the proposed deep learning network, which contains two parts: one to extract general features from the input images, while the other to generate a discriminative and compact representation for action recognition. The proposed method is tested on the SBU kinect interaction dataset, the CMU dataset, and the large-scale NTU RGB+D dataset and achieves state-of-the-art performance.
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
A New Representation of Skeleton Sequences for 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.486 · Cited by 793
Self-citationHuman Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
Self-citationSkeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
Liu, J., Wang, G., Duan, L.-Y., Abdiyeva, K., & Kot, A. C. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2785279 · Cited by 537
Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
Shu, X., Zhang, L., Qi, G.-J., Liu, W., & Tang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050918 · Cited by 276
Metadata
- Authors
- Ke, Q., An, S., Bennamoun, M., Sohel, F., & Boussaid, F.
- Venue
- IEEE Signal Processing Letters
- Published
- 2017-06
- Volume
- 24
- Issue
- 6
- Pages
- 731-735
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2602311553
- Cited by
- 172
- References
- 1
- FWCI
- 10.78
- Type
- journal article
- Language
- en
- Open access
- green
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- Ke, Q., An, S., Bennamoun, M., Sohel, F., & Boussaid, F. (2017). SkeletonNet: Mining Deep Part Features for 3-D Action Recognition. IEEE Signal Processing Letters. https://doi.org/10.1109/lsp.2017.2690339
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