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Harmonizing local and global features: enhanced hand gesture segmentation using synergistic fusion of CNN and transformer networks

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Abstract

Hand gesture segmentation is an important research topic in computer vision. Despite ongoing efforts, achieving optimal gesture segmentation remains challenging, attributed to factors like gesture morphology and intricate backgrounds. In light of these challenges, we propose a novel hand gesture segmentation approach that strategically combines the strengths of Convolutional Neural Networks (CNN) for local feature extraction and Transformer Networks for global feature integration. To be more specific, we design two feature fusion modules. One employs an attention mechanism to learn how to fuse features extracted by CNN and Transformer. The second module utilizes a combination of group convolution and activation functions to implement gating mechanisms, enhancing the response of crucial features while minimizing interference from weaker ones. Our proposed method achieves mIoU score of 93.53%, 97.25%, and 90.39% on OUHANDS, HGR1, and EgoHands hand gesture datasets respectively, which outperforms the state-of-the-art methods.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We gratefully appreciate the editor and reviewers for reviewing this manuscript. This work is partially supported by the Central Government Guided Local Funds for Science and Technology Development No.216Z0301G; National Natural Science Foundation of China No.61379065; Hebei Natural Science Foundation No.F2023203012; Science Research Project of Hebei Education Department No. QN2024010; Innovation Capability Improvement Plan Project of Hebei Province No.22567626H.

Funding

Central Government Guided Local Funds for Science and Technology Development (216Z0301G), National Natural Science Foundation of China (61379065), Hebei Natural Science Foundation (F2023203012), Science Research Project of Hebei Education Department (QN2024010), Innovation Capability Improvement Plan Project of Hebei Province (22567626H).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Hebei Street, Qinhuangdao, 066004, Hebei, China

    Shi Wang, Ning Yang & Shihui Zhang

  2. School of Electrical and Computer Engineering, University of Georgia, 200 D.W. Brooks Drive, Athens, GA, 30602, USA

    Maohua Liu

  3. Department of Computer Science, University of Alabama at Birmingham, 1720 2nd Avnue South, Birmingham, AL, 35294, USA

    Qing Tian

  4. Artificial Neural Network Technology Application and Innovation Team of School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Hebei Street, Qinhuangdao, 066004, Hebei, China

    Shi Wang & Maohua Liu

  5. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Hebei Street, Qinhuangdao, 066004, Hebei, China

    Shihui Zhang

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  1. Shi Wang
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  2. Ning Yang
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Contributions

SW wrote the main manuscript text and wrote codes. NY prepared the data and figures. ML edited the manuscript. QT validated the manuscript. SZ directed this study. All authors reviewed the manuscript.

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Correspondence to Shihui Zhang.

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Wang, S., Yang, N., Liu, M. et al. Harmonizing local and global features: enhanced hand gesture segmentation using synergistic fusion of CNN and transformer networks. SIViP (2024). https://doi.org/10.1007/s11760-024-03255-5

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