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

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.

1. Clone this repository

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

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

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

5. If you are using Google Colab upload all the files to Google Drive, including yolov3 weights from step 2.
6. Create a Google Colaboratory Notebook in the same directory as predict_video.py, change Runtime Type to GPU 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('drive/MyDrive/Colab Notebooks/tennis-tracking')

8. Install only 2 requirements, because Colab already has the rest

!pip install filterpy sktime

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 --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=1 bounce 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 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
• 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)
  
  • 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!

References

• Yu-Chuan Huang, "TrackNet: Tennis Ball Tracking from Broadcast Video by Deep Learning Networks," Master Thesis, advised by Tsì-Uí İk and Guan-Hua Huang, National Chiao Tung University, Taiwan, April 2018.

• Yu-Chuan Huang, I-No Liao, Ching-Hsuan Chen, Tsì-Uí İk, and Wen-Chih Peng, "TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications," in the IEEE International Workshop of Content-Aware Video Analysis (CAVA 2019) in conjunction with the 16th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2019), 18-21 September 2019, Taipei, Taiwan.

• Joseph Redmon, Ali Farhadi, "YOLOv3: An Incremental Improvement", University of Washington, https://arxiv.org/pdf/1804.02767.pdf