Automated Tennis Player and Ball Tracking with Court Keypoints Detection (Hawk Eye System)
Venkata Manikanta Desu, Syed Fawaz Ali

TL;DR
This paper introduces an integrated deep learning system for real-time tennis match analysis, detecting and tracking players, balls, and court keypoints to provide detailed performance insights.
Contribution
It presents a novel pipeline combining YOLOv8, YOLOv5, and ResNet50 models for comprehensive tennis match analysis in real time.
Findings
Robust detection and tracking across different court conditions
Accurate analysis of player movements and ball dynamics
System outputs actionable insights for coaching and broadcasting
Abstract
This study presents a complete pipeline for automated tennis match analysis. Our framework integrates multiple deep learning models to detect and track players and the tennis ball in real time, while also identifying court keypoints for spatial reference. Using YOLOv8 for player detection, a custom-trained YOLOv5 model for ball tracking, and a ResNet50-based architecture for court keypoint detection, our system provides detailed analytics including player movement patterns, ball speed, shot accuracy, and player reaction times. The experimental results demonstrate robust performance in varying court conditions and match scenarios. The model outputs an annotated video along with detailed performance metrics, enabling coaches, broadcasters, and players to gain actionable insights into the dynamics of the game.
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Taxonomy
TopicsVideo Analysis and Summarization · Human Pose and Action Recognition · Sports Dynamics and Biomechanics
