Tennis Tracking 🎾
With ❤️ by ArtLabs
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 ResNet50 was used. See ArtLabs/projects for more or similar projects.
Example using sample videos
| Input | Output |
|---|---|
.gif) |
 |
.gif) |
.gif) |
 |
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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.
- [ ] Input videos have to be rallies of the game and shouldn't contain any commercials, breaks or spectators.
- Clone this repository ```git git clone https://github.com/ArtLabss/tennis-tracking.git ```
- Download yolov3 weights (237 MB) from here and add it to your Yolov3 folder.
- Install the requirements using pip ```python pip install -r requirements.txt ```
- Run the following command in the command line ```python python3 predict_video.py --input_video_path=VideoInput/video_input3.mp4 --output_video_path=VideoOutput/video_output.mp4 --minimap=0 --bounce=0 ```
- If you are using Google Colab upload all the files to Google Drive, including yolov3 weights from step 2.
-
Create a Google Colaboratory Notebook in the same directory as
predict_video.py, change Runtime Type to GPU and connect it to Google drive
```python
from google.colab import drive
drive.mount('/content/drive')
```
-
Change the working directory to the one where the Colab Notebook and
predict_video.pyare. In my case,
```python
import os
os.chdir('drive/MyDrive/Colab Notebooks/tennis-tracking')
```
- Install only 2 requirements, because Colab already has the rest ```python !pip install filterpy sktime ```
-
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 --bounce=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 |
|---|---|
 |
 |
To predict bounce points machine learning library for time series sktime was used. Specifically, TimeSeriesForestClassifier was trained on 3 variables: x, y coordinates of the ball and V for velocity (V2-V1/t2-t1). Data for training the model - df.csv
- By specifiying
--bounce=1bounce points can be detected and displayed
The model predicts true negatives (not bounce) with accuracy of 98% and true positives (bounce) with 83%.
Further Developments
Improve line detection of the court and remove overlapping linesAlgorithm fails to detect players when the court colors aren't similar to the sample videoDon't detect the ballboys/ballgirlsDon't contour the bannersFind 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 minutes16 minutes)- Instead of writing a new video, a faster way would be to show the frame right after it has been processed
- Algorithm works only on official match videos
Helpful Repositories
- Tennis Tracking @MaximeBataille
- Tennis Project @avivcaspi
- TrackNet
Contribution
Help us by contributing, check out the CONTRIBUTING.md. Contributing is easy!