RESEARCH PAPER
Computer Vision-Based Analysis of Tennis contact point Characteristics for Performance Evaluation
1
Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
2
Department of Computer Science & Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
3
Physical Education Office, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Submission date: 2025-04-10
Final revision date: 2025-07-24
Acceptance date: 2026-05-07
Publication date: 2026-07-16
Corresponding author
Wei-Ting Lin
Physical Education Office, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Physical Education Office, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
KEYWORDS
TOPICS
ABSTRACT
Precise timing of ball-racket contact is a critical determinant of stroke performance in tennis, yet few studies have objectively quantified this variable across different players’ groups. This study developed and validated a hybrid vision-based framework combining K-nearest neighbors (KNN) background subtraction with Hue-Saturation-Value (HSV) color segmentation to analyze the spatial relationship between the contact point and the apex of the ball’s trajectory. Twelve participants were recruited and stratified by skill level and gender, each performing 30 forehand and 30 backhand strokes, yielding 720 stroke events. Contact point overlap ratios and vertical height distributions were extracted, and two-way ANOVA was used to examine main and interaction effects. Results indicated a significant effect of the skill level, with top-level players demonstrating higher overlap ratios and reduced vertical deviations compared to average-level players (p < 0.001, partial η² = 0.386). Gender differences were negligible among top-level players but pronounced in the average-level group, particularly for backhand strokes (p < 0.001). These findings highlight the skill level as the dominant factor influencing contact point precision, while gender disparities emerge primarily among less experienced athletes. The proposed computer vision approach offers a low-cost and scalable tool for objective stroke assessment and has potential applications in individualized training feedback and performance monitoring.
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