Pose2Trajectory: Using transformers on body pose to predict tennis player’s trajectory

AlShami, Ali; Boult, Terrance; Kalita, Jugal

doi:10.1016/j.jvcir.2023.103954

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…Unlike the authors' work, they categorized different key points of the human body to assess an athlete's posture. AlShami et al (2023) applied a Transformer to predict a tennis player's future trajectory as a sequence derived from their body joints' data and ball position. Although the trajectory tracking task is similar, the figure subject in the picture occupies a large proportion, and the detection is relatively easy.…”

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confidence: 99%

A Semantic Web-Based Approach for Bat Trajectory Reconstruction With Human Keypoint Information

Jin,

Zheng,

Liu

et al. 2024

International Journal on Semantic Web and Information Systems

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Restoring the trajectory of a bat from a table tennis match video is critical in analyzing a table tennis technique and conducting statistical analysis. However, directly bat location detection in each frame is challenging due to changing shapes caused by varying movement directions and speeds, leading to ambiguity. This paper develops a novel two-stage method. The first stage utilizes YOLO for bat detection in each frame, followed by filtering out erroneous candidate boxes. In the second stage, the authors use a temporal prediction model that integrating human keypoint information and interpolation to reconstruct a complete bat trajectory with minimal errors. The method's effectiveness and performance are evaluated on our video datasets. The evaluation results demonstrate that the proposed method outperforms traditional methods on precision performance metrics. The error screening algorithm improves precision score to nearly 1. In addition, the method has the recall score 22.3% higher than YOLO 's and also 1.4% higher than that of YOLO with cubic spline interpolation.

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confidence: 99%