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LGCANet: lightweight hand pose estimation network based on HRNet

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Abstract

Hand pose estimation is a fundamental task in computer vision with applications in virtual reality, gesture recognition, autonomous driving, and virtual surgery. Keypoint detection often relies on deep learning methods and high-resolution feature map representations to achieve accurate detection. The HRNet framework serves as the basis, but it presents challenges in terms of extensive parameter count and demanding computational complexity due to high-resolution representations. To mitigate these challenges, we propose a lightweight keypoint detection network called LGCANet (Lightweight Ghost-Coordinate Attention Network). This network primarily consists of a lightweight feature extraction head for initial feature extraction and multiple lightweight foundational network modules called GCAblocks. GCAblocks introduce linear transformations to generate redundant feature maps while concurrently considering inter-channel relationships and long-range positional information using a coordinate attention mechanism. Validation on the RHD dataset and the COCO-WholeBody-Hand dataset shows that LGCANet reduces the number of parameters by 65.9% and GFLOPs by 72.6% while preserving the accuracy and improves the detection speed.

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Author notes

  1. Shoukun Li, Hao Wang, Beibei Wang and Haoyi Wang have contributed equally.

Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Xiaoying Pan, Shoukun Li & Beibei Wang

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Xiaoying Pan, Shoukun Li & Beibei Wang

  3. School of Software, Northwestern Polytechnical University, Xi’an, 710121, Shaanxi, China

    Hao Wang

  4. National Engineering Laboratory for Air Earth-Sea Integration Big Data Application Technology, Northwestern Polytechnical University, Xi’an, 710121, Shaanxi, China

    Hao Wang

  5. Westa College, Southwest University, Chongqing, China

    Haoyi Wang

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  1. Xiaoying Pan
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  2. Shoukun Li
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Correspondence to Xiaoying Pan.

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Pan, X., Li, S., Wang, H. et al. LGCANet: lightweight hand pose estimation network based on HRNet. J Supercomput 80, 19351–19373 (2024). https://doi.org/10.1007/s11227-024-06226-2

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  • DOI: https://doi.org/10.1007/s11227-024-06226-2

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