AI-Driven Tennis Coaching

The objective of this project is to create a video assistant feature for tennis coaching by finding professional tennis videos that are similar to individuals; this is a domain yet unexplored. This application is developed by utilizing the ViViT: Video Vision Transformer for advanced video embedding, coupled with a K-Nearest Neighbors (KNN) algorithm, to match user-recorded tennis videos with professional videos. The ViViT model allows for the extraction of only the needed token from each video, an efficient approach to managing the complexities of sophisticated large data. To match these user-recorded videos with an ideal set of professional coaching videos, KNN is used to efficiently search through a database of professional video embeddings to find the closest matches to the user’s video. The pipeline designed for the AI-driven tennis coaching system includes a feedback loop for continuous improvement. When the algorithm fails to match videos accurately, it triggers a retraining protocol. New data from these mismatches are added to the training set, allowing the model to learn from its mistakes and enhance its performance over time. This self-improving system ensures the application evolves and adapts to provide increasingly accurate video matches for tennis coaching. The result is a successful algorithm that personalized approach that facilitates a unique learning experience, enabling players to visually compare their techniques with professional standards, identify areas of improvement, and adopt high-level strategies.