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Motion trajectory tracking of athletes with improved depth information-based KCF tracking method

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Abstract

In this paper, a computer vision-based technique is proposed to track the motion trajectories of athletes to improve sport performance and deliver reliable physical data analysis. The moving target in the video was obtained through the Universal Background Eliminator (ViBe) moving target detection algorithm, and fused with the associated depth information based on the Kernerlized Correlation Filter (KCF) tracking algorithm. In order to better identify partly occluded objects, the depth information of the moving target and background segmentation threshold were acquired based on the depth distribution features within the original video tracking range. Meanwhile, the number of negative samples in the training sample set was reduced to prevent the model shifting problem caused by the background and obstacles. Experiments have been conducted on the dance trajectories of gymnasts as well as the motion trajectories of batting arms of badminton players. The results prove that the proposed algorithm can reduce the trajectory prediction error and improve the operating efficiency.

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  1. Zhejiang College of Security Technology, ZheJiang, China

    Lina Zhang & Haidong Dai

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  1. Lina Zhang
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  2. Haidong Dai
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Correspondence to Lina Zhang.

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Zhang, L., Dai, H. Motion trajectory tracking of athletes with improved depth information-based KCF tracking method. Multimed Tools Appl 82, 26481–26493 (2023). https://doi.org/10.1007/s11042-023-14929-6

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  • DOI: https://doi.org/10.1007/s11042-023-14929-6

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