Transforming tennis footage with AI insights from ChatGPT and statistical analysis. 🎾 Elevate your game with dynamic visuals and strategic summaries. 🚀
Our inspiration for TRACY came from the desire to enhance tennis training through advanced technology. One of our members was a former tennis enthusiast who has always strived to refine their skills. They soon realized that the post-game analysis process took too much time in their busy schedule. We aimed to create a system that not only analyzes gameplay but also provides personalized insights for players to improve their skills.
TRACY utilizes computer vision algorithms and pre-trained neural networks to analyze tennis footage, tracking player movements, and ball trajectories. The system then employs ChatGPT for AI-driven insights, generating personalized natural language summaries highlighting players' strengths and weaknesses. The output includes dynamic visuals and statistical data, offering a comprehensive overview and further insights into the player's performance.
3rd place winner in QHacks 2024
- Introducing TRACY: Tennis Real-time Analysis Coaching
- Table of Contents
- Getting Started
- Contributors
- FFmpeg
- yolov3 weights
- Node.js
- React.js
- Axios
- Python 3.x
- Flask
- Tensorflow (Keras)
- OpenCV
- OpenAI
Ensure that a .env file is created in the project directory with your OpenAI key, under the key "OPENAI_API_KEY". ChatGPT 4 will be used.
In the project directory, you can run:
Runs the web app in the development mode.
Open http://localhost:3000 to view it in your browser.
The page will reload when you make changes.
You may also see any lint errors in the console.
Runs the ChatGPT API server that posts a response from ChatGPT to the front end.
- Vu Cao: @mizly
- Daniel Lu: @FinityFly
- Edwin Ngui: @EdwinNgui