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Mobile-LRPose: Low-Resolution Representation Learning for Human Pose Estimation in Mobile Devices

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14429))

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Abstract

Human pose estimation has made great progress in performance due to the development of deep learning. Current methods, including some lightweight networks, usually generate high-resolution heatmaps with rich position information to ensure high accuracy, however, the computational cost is heavy and sometimes unacceptable to mobile devices. In this paper, we construct a network backbone based on the modified MobileNetV2 to only generate low-resolution representations. Then, to enhance the capability of keypoints localization for our model, we also make crucial improvements consisting of bottleneck atrous spatial pyramid, local-space attention, coordinate attention and position embedding. In addition, we design two different network heads for 2D and 3D pose estimation to explore the extensibility of the backbone. Our model achieves superior performance to state-of-the-art lightweight 2D pose estimation models on both COCO and MPII datasets, which achieves 25+ FPS on HUWEI Kirin 9000 and outperforms MoveNet in the same device. Our 3D model also makes nearly 50% and 90% reduction on parameters and FLOPs compared to lightweight alternatives. Code is available at: https://github.com/NanXinyu/Mobile_LRPose.git.

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Acknowledge

This work is supported by the science and technology project fundings of State Grid Jiangsu Electric Power Co., Ltd. (J2023031).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, China

    Xinyu Nan & Chenxing Wang

Authors
  1. Xinyu Nan
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  2. Chenxing Wang
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Corresponding author

Correspondence to Chenxing Wang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Nan, X., Wang, C. (2024). Mobile-LRPose: Low-Resolution Representation Learning for Human Pose Estimation in Mobile Devices. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_17

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  • DOI: https://doi.org/10.1007/978-981-99-8469-5_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8468-8

  • Online ISBN: 978-981-99-8469-5

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