Paper
19 July 2024 KPLitePose: fast and lightweight 3D human pose estimation
Shichen Wang, Zhigang Chen, Wendong Zhang
Author Affiliations +
Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133M (2024) https://doi.org/10.1117/12.3035509
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
The 3D human pose estimation algorithm achieves very high detection accuracy on standard datasets. However, the challenge of reducing model parameters and computational load without compromising accuracy remains a central focus of ongoing research. To address this problem, we propose a two-stage lightweight 3D human pose estimation framework, KPLitePose, which is characterized by a minimal number of parameters, reduced computational requirements, high accuracy, and fast convergence speed. In the backbone network of this framework, we designed KPLiteNet, a lightweight feature extraction network, which performs well in feature extraction while maintaining the lightweight nature of the model. Finally, a multi-view key point fusion method is proposed to reduce the impact of 2D key point noise on the results. We evaluated the performance of the framework on the Campus and Shelf datasets, achieving an inference speed of 65FPS on the Shelf dataset with a PCP3D score of 97.2%. Our framework cleverly balances inference speed and detection accuracy.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Shichen Wang, Zhigang Chen, and Wendong Zhang "KPLitePose: fast and lightweight 3D human pose estimation", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133M (19 July 2024); https://doi.org/10.1117/12.3035509
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KEYWORDS
Pose estimation

Cameras

RGB color model

Convolution

Feature extraction

3D modeling

Data modeling

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