AI-Powered Tennis Match Analysis system with YOLO, PyTorch, and Key Point Extraction
NetGenius: AI-Powered Tennis Match Analysis System
NetGenius is an advanced AI-powered system designed to analyze tennis matches by detecting and tracking players and the tennis ball in videos. It utilizes state-of-the-art machine learning techniques, including YOLO for object detection and CNNs for court keypoint extraction, to provide comprehensive insights into player performance.

Output video

Features
- Player and Ball Detection: Detects and tracks players and the tennis ball using YOLO.
- Court Keypoint Extraction: Extracts keypoints of the tennis court using CNNs.
- Player and Ball Statistics: Calculates various statistics such as player speed, ball shot speed, and the number of shots.
- Visual Output: Generates annotated videos with detailed visualizations of detections and statistics.
Project Structure
The project is organized into several stages:
- Input Pipeline: Loads and preprocesses the input video.
- Player and Ball Detection: Detects players and the ball in video frames.
- Courtline Detection: Identifies court lines and keypoints.
- Minicourt Construction: Builds a scaled-down representation of the court for easier analysis.
- Player Stats Calculation: Computes various player and ball statistics.
- Output Drawing: Annotates the video with detected objects and statistics.
Dataset used for training the model
This project uses the following dataset:
tennis ball detection Dataset
- Clone the repository:
git clone https://github.com/yash-raj202134/NetGenius.git
cd NetGenius
- Activate env:
- run the main script:
Requirements
To execute this project, you must have the following dependencies installed:
(see requirements.txt)
License
This project is licensed under the GNU License. See the LICENSE file for more details.
Author
Feel free to contact :
- Email : [yashraj3376@gmail.com]