Low-budget real-time pose estimation method for personal physical exercise assistant

Zhaodong Bing; Han Wang; Yixing Su; Shengjin Wang

doi:10.1117/12.2589506

27 January 2021 Low-budget real-time pose estimation method for personal physical exercise assistant

Zhaodong Bing, Han Wang, Yixing Su, Shengjin Wang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200V (2021) https://doi.org/10.1117/12.2589506
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Pose estimation is a fundamental task in the field of computer vision. It contains a huge variety of sub-tasks, including 3D pose estimation, pose tracking, etc. In this paper, we propose a novel algorithm of pose estimation to determine whether it is correct of doing certain physical exercises by single-person pose-tracking and key frame extraction method. First, we use the proposed tracking compensation method to refine the output of pose estimation network. Second, we define different angles composed of human key joints as action angles to determine the standards of physical exercises. Therefore, we can have a personal physical exercise assistant and do physical exercises even without professional personal trainer around. Experiments show that our real-time method can achieve 92.0% accuracy on two kinds of physical exercise actions. It can be adapted to different applications with important significance in theory and practice.

Citation Download Citation

Zhaodong Bing, Han Wang, Yixing Su, and Shengjin Wang "Low-budget real-time pose estimation method for personal physical exercise assistant", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200V (27 January 2021); https://doi.org/10.1117/12.2589506

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