How Can Real-Time Computer Vision Models Accurately Detect and Track Player and Ball Movement in Tennis Matches, and How Do Their Performance and Reliability Vary Under Different Match Conditions?
- Avighna Daruka
- Feb 13
- 1 min read
Arush Singhania (1)
(1) Student, Saint Stephen's Episcopal School, Bradenton, FL
This study examines real-time computer vision models for detecting and tracking player and ball movements in tennis matches under varying conditions, including changes in lighting, camera angles, occlusion, and motion blur. Accurate detection and tracking are essential for performance analytics, automated broadcasting, and sports officiating. The proposed Real-Time Tennis Analytics System (TAS) integrates YOLOv8 with motion tracking frameworks such as SORT and DeepSORT to achieve high accuracy and low latency match analysis. YOLOv8 detects players, the ball, and court lines, while a motion analysis module computes player trajectories, shot speeds, rally durations, and positional patterns.
Models are trained on diverse professional and synthetic match footage annotated with player and ball positions. Performance is evaluated using precision, recall, Multiple Object Tracking Accuracy (MOTA), and Frames Per Second (FPS). TAS achieves high accuracy under stable lighting and camera conditions, though reliability decreases during high-speed rallies, shadowed courts, or low-resolution footage. The study highlights trade-offs between model complexity and responsiveness and proposes adaptive data augmentation and temporal smoothing to improve robustness. Overall, TAS shows that combining YOLOv8 detection with motion analytics provides real-time insights for coaches, analysts, and broadcasters, supporting data-driven decisions in tennis and other racket sports.
Keywords: YOLOv8; Tennis Analytics; Object Detection; Motion Tracking; Computer Vision; Sports AI
Article Type: Original Research
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Interesting read on how YOLOv8 combined with DeepSORT handles real-time tennis tracking, especially with challenges like occlusion, motion blur, and fast rallies. When taking a break from technical papers like this, I usually switch to something light like Subway Surfers in my free time. It’s a simple game but pretty fun and relaxing to unwind with.
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This is a fascinating exploration! I wonder how integrating player psychology data might further enhance the accuracy of your models. ragdoll playground It could lead to even more dynamic insights during matches.
I'm fascinated by how real-time computer vision can enhance sports analysis! I wonder, how would these models perform in varying lighting conditions? Also, have you considered their potential application in games like Slope 2? The fast-paced movement and angles there could present unique challenges.