OneCourt
The purpose of this repository is to store the object and event detection models for various sports.
Overview
Ball Tracking
Demo
- Description: Tracks a tennis ball using the HSV color live using a laptop camera. For demo purposes only.
- Filepath: tennis\demo\live_ball_tracking.py
- Input: Live camera feed
- Output:
- in terminal, prints X and Y coordinates of the pixel location on the camera
- Opens local camera and displays visual output of ball tracking
TrackNet
- TrackNet is a CNN model trained to track tennis ball location, both using a single frame (TrackNet I) and multiple frames (TrackNet II) as input.
- Original Paper: TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
- Source code & Dataset: https://nol.cs.nctu.edu.tw/ndo3je6av9/
- Filepath: tennis\models\TrackNet
- Input: tennis video path
- Output: heatmap frames of predicted tennis ball location
UNET
- Documentation: tennis\models\UNET\documentation
- Filepath: tennis\models\UNET
- Input: tennis video path
- Output: frames of predicted tennis ball location
Action Tracking
Apply Homogenous Projection
Tracking with Music
Player Tracking
- UNet Model
- LSTM Model
Tennis Key milestones
- Train tennis model to track ball location
- Train tennis model to track player locations
- Train tennis model to track key actions (bounce, hit, air)
- Homography estimations for tennis court positions
- Train tennis model to track game events (score, out, etc.)
- Explore designing a single model architecture to track all of the details that we want to improve inference and scalability
- Upload model to server and connect it to the device
- Test the complete-end-to-end process with our own generated tennis footage*
- Research how to improve homographic projections
- Explore ways to improve overall performance and inference
- Test process end-to-end in a live tennis match on UW tennis court*
Sources
Tennis:
- Tennis Tracking: https://github.com/ArtLabss/tennis-tracking
- TrackNet: https://arxiv.org/abs/1907.03698
- YOLOv3: https://pjreddie.com/darknet/yolo/