Tennis Tracking 🎾

Objectives

• Track the ball
• Detect court lines
• Detect the players

To track the ball we used TrackNet - deep learning network for tracking high-speed objects. For players detection yolov3 was used.

Example using sample videos

Input	Output

How to run

This project requires compatible GPU to install tensorflow, you can run it on your local machine in case you have one or use Google Colaboratory with Runtime Type changed to GPU.

1. Clone this repository

git clone https://github.com/ArtLabss/tennis-tracking

2. Download yolov3 weights (237 MB) from here and add it to your Yolov3 folder.
3. Install the requirements using pip

pip install -r requirements.txt

4. Run the following command in the command line

python predict_video.py --input_video_path=VideoInput/video_input3.mp4 --output_video_path=VideoOutput/video_output.mp4 --minimap=0

5. If you are using Google Colab upload all the files to Google Drive
6. Create a Google Colaboratory Notebook in the same directory as predict_video.py and connect it to Google drive

from google.colab import drive
drive.mount('/content/drive')

7. Change the working directory to the one where the Colab Notebook and predict_video.py are. In my case,

import os 
os.chdir('MyDrive/Colab Notebooks/tennis-tracking')

8. Install the requirements

!pip install -r requirements.txt

9. Inside the notebook run predict_video.py

 !python3 predict_video.py --input_video_path=VideoInput/video_input3.mp4 --output_video_path=VideoOutput/video_output.mp4 --minimap=0

After the compilation is completed, a new video will be created in VideoOutput folder if --minimap was set 0, if --minimap=1 three videos will be created: video of the game, video of minimap and a combined video of both

P.S. If you stumble upon an error or have any questions feel free to open a new Issue

What's new?

• Court line detection improved
• Player detection improved
• The algorithm now works practically with any court colors
• Faster algorithm
• Dynamic Mini-Map with players and ball added, to activate use argument --minimap

--minimap=0	--minimap=1

Further Developments

• Improve line detection of the court and remove overlapping lines
• Algorithm fails to detect players when the court colors aren't similar to the sample video
• Don't detect the ballboys/ballgirls
• Don't contour the banners
• Detect players on videos with different angles
• Find the coordinates of the ball touching the court and display them
• Code Optimization
• Dynamic court mini-map with players and the ball

Current Drawbacks

• Slow algorithms (to process 15 seconds video (6.1 Mb) it takes 28 minutes 16 minutes)
• Algorithm works only on official match videos

References