The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Learning Clip Representations for Skeleton-Based 3D Action Recognition
Abstract
This paper presents a new representation of skeleton sequences for 3D action recognition. Existing methods based on hand-crafted features or recurrent neural networks cannot adequately capture the complex spatial structures and the long-term temporal dynamics of the skeleton sequences, which are very important to recognize the actions. In this paper, we propose to transform each channel of the 3D coordinates of a skeleton sequence into a clip. Each frame of the generated clip represents the temporal information of the entire skeleton sequence and one particular spatial relationship between the skeleton joints. The entire clip incorporates multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We also propose a multitask convolutional neural network (MTCNN) to learn the generated clips for action recognition. The proposed MTCNN processes all the frames of the generated clips in parallel to explore the spatial and temporal information of the skeleton sequences. The proposed method has been extensively tested on six challenging benchmark datasets. Experimental results consistently demonstrate the superiority of the proposed clip representation and the feature learning method for 3D action recognition compared to the existing techniques.
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Cites
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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
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Cited by
Human 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 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
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., & Tang, H. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413941 · Cited by 341
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
Chen, Z., Li, S., Yang, B., Li, Q., & Liu, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16197 · Cited by 339
Metadata
- Authors
- Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-06
- Volume
- 27
- Issue
- 6
- Pages
- 2842-2855
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2793547936
- Cited by
- 270
- References
- 8
- FWCI
- 15.83
- Type
- journal article
- Language
- en
- Open access
- closed
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