SP-YOLO: an end-to-end lightweight network for real-time human pose estimation
Author:
Funder
the National Ministry of Science and Technology Innovation Method Special
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02812-8.pdf
Reference30 articles.
1. Li, Y., Jia, S., Li, Q.: BalanceHRNet: an effective network for bottom-up human pose estimation. Neural Netw. 161, 297–305 (2023). https://doi.org/10.1016/j.neunet.2023.01.036
2. Miki, D., Abe, S., Chen, S., et al.: Robust human pose estimation from distorted wide-angle images through iterative search of transformation parameters. SIViP 14, 693–700 (2020). https://doi.org/10.1007/s11760-019-01602-5
3. Lu, H., Shao, X., Xiao, Y.: Pose estimation with segmentation consistency. IEEE Trans. Image Process. 22(10), 4040–4048 (2013). https://doi.org/10.1109/TIP.2013.2268975
4. Wan, T., Luo, Y., Zhang, Z., et al.: TSNet: tree structure network for human pose estimation. SIViP 16, 551–558 (2022). https://doi.org/10.1007/s11760-021-01999-y
5. Ke, X., Liu, T., Li, Z.: Human attribute recognition method based on pose estimation and multiple-feature fusion. SIViP 14, 1441–1449 (2020). https://doi.org/10.1007/s11760-020-01690-8
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