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Fast moving table tennis ball tracking algorithm based on graph neural network

Fast moving table tennis ball tracking algorithm based on graph neural network

 



. Nov 26, 2024;14(1):29320.

two: 10.1038/s41598-024-80056-3.

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Tianjian Zou et al.

Scientific representative.


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Abstract

Tracking key objects in sports video scenarios poses a crucial challenge in the analysis of sports techniques and tactics. In table tennis, due to the small size and fast movement of the ball, identifying and tracking the table tennis ball via video is a particularly difficult task, with the majority of existing detection and tracking algorithms struggling to meet the practical application requirements in practice to comply. world scenarios. To address this problem, this paper proposes a combined engineering approach that integrates detection and discrimination, tailored to the unique motion characteristics of table tennis. For the detector, we use and refine a common video differential detector. As for the discriminator, we introduce GMP (a Graph Max-message Pass Neural Network), which is specifically designed for tracking table tennis balls or similar objects. Furthermore, we improve an existing dataset for table tennis tracking problems by enriching its scenarios. The results show that our proposed technical solution performs impressively on both the dataset and the targeted real-world environments, demonstrating the good scalability of our algorithms and models, as well as their potential for application in other scenarios.

Keywords:

Fast moving object; Neural network graph; Object detection; Object tracking; Sports analysis; Table tennis.

2024. The Author(s).

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Conflict of Interest Statement

Statements. Competing interests: The authors declare no competing interests. Consent for publication: We confirm that we obtained informed consent from all subjects and/or their legal guardians for the publication of personal information/images in openly accessible online publications, and that data collection was only conducted after obtaining their informed consent. Informed consent: We confirm that all methods were performed in accordance with relevant guidelines and regulations. We confirm that all protocols have been approved by the Working Committee on Ethics and Governance of Artificial Intelligence of the Chinese Association for Artificial Intelligence. We confirm that informed consent was obtained from all subjects and/or their legal guardian(s).

References

    1. Qiu, X., Zhang, H., Wei, J., Liu, J.: Machine learning-based motion analysis and correction for table tennis. In: 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS), pp. 150154 (2022). IEEE

    1. Zheng, Y., Zhou, W., Zou, T., Zhang, H.: A table tennis bat court reconstruction method with the fusion of human keypoint information. In: 2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), pp. 7175 (2023). IEEE

    1. Yu, B., Wei, J., Zhang, H., Jin, Z., Yu, Y., Wang, Q., Liu, J.: A machine learning-based method to establish a normative action model for table tennis to set. In: 2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), pp. 9195 (2023). IEEE

    1. Cores, D., Brea, VM & Mucientes, M. Spatiotemporal tubelet provide aggregation and object linking for small object detection in videos. Applied Intelligence 53(1), 12051217 (2023). –

      DOI

    1. Rozumnyi, D., Kotera, J., Sroubek, F., Novotny, L., Matas, J.: The world of fast-moving objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 52035211 (2017)

Sources

1/ https://Google.com/

2/ https://pubmed.ncbi.nlm.nih.gov/39592713/

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