Tennis Stroke Recognition: A Comprehensive Survey Using Computer Vision

This survey paper provides a comprehensive overview of the latest advancements in tennis stroke recognition using computer vision techniques. It covers various aspects of the research, including:

• Data Acquisition and Preprocessing: Discusses methods for acquiring and preparing tennis stroke data for analysis, including video capture, image extraction, and noise reduction.
• Feature Extraction: Explores different feature extraction techniques, both traditional (e.g., HOG, SIFT) and deep learning-based, for characterizing tennis strokes.
• Classification Algorithms: Reviews popular classification algorithms used for identifying different strokes, such as Support Vector Machines (SVMs), Random Forests, and Convolutional Neural Networks (CNNs).
• Performance Evaluation: Presents metrics and benchmarks for evaluating the accuracy and efficiency of different stroke recognition systems.
• Challenges and Future Directions: Highlights the challenges and limitations of current techniques and proposes promising avenues for future research in this area.

The paper also discusses potential applications of stroke recognition technology, such as:

• Real-time stroke analysis: Providing immediate feedback to players during training or competition.
• Automated coaching systems: Offering personalized coaching based on stroke performance analysis.
• Improved sports broadcasting: Enhancing viewer experience with real-time stroke identification and analysis.

This survey serves as a valuable resource for researchers and practitioners interested in exploring the use of computer vision for analyzing tennis strokes. It provides a thorough overview of the current state of the art, identifies key challenges, and suggests potential future research directions. The paper also highlights the practical implications of stroke recognition technology in various applications, demonstrating its potential to revolutionize the way we understand and analyze tennis.