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Video based basketball shooting prediction and pose suggestion system

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Abstract

Video based motion analysis, which aims to acquire the whole posture data by simple camera and without placing sensors on the body parts, has become the major analysis method in the sport domain. However, most video based motion analysis approaches either work only for some specific domain action recognition, or suffer from low prediction rates for practical applications in the sport domain. This paper presents an effective system to predict basketball shooting and to suggest corrected postures, as based on video based motion analysis with the OpenPose system. Given a basketball shooting video sequences, the proposed system first detects the human joint points acquired from the OpenPose system, and then, the video frames of the shooting period are detected by two important features of the shooting process. Basketball shooting is predicted using the adopted trajectory curves matching method and the K-nearest neighbor classification method. Finally, the wrong shooting posture is corrected and suggested based on the pix2pix conditional GAN (cGAN) model. Experimental results show that our approach can effectively estimate shooting results with high accuracy.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, No.151, Yingzhuan Rd., Tamsui Dist., New Taipei City, 25137, Taiwan, Republic of China

    Chien-Chang Chen, Chen Chang & Cheng-Shian Lin

  2. Office of Physical Education, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist., New Taipei City, 25137, Taiwan, Republic of China

    Chien-Hua Chen & I. Cheng Chen

  3. National Taiwan Normal University, Taipei City, Taiwan, Republic of China

    Chien-Hua Chen

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  1. Chien-Chang Chen
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  2. Chen Chang
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  3. Cheng-Shian Lin
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  4. Chien-Hua Chen
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Correspondence to Cheng-Shian Lin.

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Chen, CC., Chang, C., Lin, CS. et al. Video based basketball shooting prediction and pose suggestion system. Multimed Tools Appl 82, 27551–27570 (2023). https://doi.org/10.1007/s11042-023-14490-2

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  • DOI: https://doi.org/10.1007/s11042-023-14490-2

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