Predicting basketball shot probability using modern computer vision technology. The goal of this project is to first develop a tool similar to HomeCourt and expand on shot predictions instead of just shot tracking.
Python 3.8.18
The datasets used to train are located:
- Here...
The trained models can be found
- Here...
Used labelmestudio.
Put video in ...
python main.py --input_video './videos/indoor2.mp4' --save_frames True --output_dir ./output/run1 --show_stats True
- Set up simple LSTM RNN for shot prediction based on keypoints.
- User frontend built using React to allow users to use.
- Build mobile application
- Basketball shot arc prediction: (1) Linear regression (2) Physics?
- Improved method to handle frames where ball is not detected?
- Improved ball and rim detection with bigger dataset.
- Simple state (holding, shot, score) recognition using overlap of bounding boxes (ball on person, ball on rim).
- Add some UI support for displaying detection
- Proof of concept with Yolov8 for pose detection, ball detection and Rim Detection
- Strictly python backend. Run through video frame by frame with CV2. Run inference using trained models.
- Use two seperate models, one to perform pose detection, one to perform ball and rim detection