This repository holds the task specific software made public for my PhD under the DeepSport project funded by the Walloon Region of Belgium, Keemotion and SportRadar. It goes along with several open-source libraries that I developed during that time:

• calib3d: a library to ease computations in 2D and 3D homogenous coordinates and projective geometry with camera calibration.
• aleatorpy: a library to control randomness in ML experiments.
• experimentator: a library to run DL experiments.
• pyconfyg: a library to describe configuration file with python.
• deepsport-utilities: the toolkit for the datasets published during the deepsport project.

Installation

Clone and install the repository with

git clone https://github.com/gabriel-vanzandycke/deepsport.git
cd deepsport
conda create --name deepsport python=3.8   # optional
pip install -e .

Setup your environment by copying .env.template to .env and set the following variables:

• LOCAL_STORAGE to the folder in which downloaded datasets should be stored.
• DATA_PATH to the list of folders to find datasets or configuration files, ordered by lookup priority.
• RESULTS_FOLDER to the full path to a folder in which outputs will be written (weigths and metrics).

Note: Environment variable like ${HOME} must be enclosed in curly brackets.

Datasets

The different tasks rely on datasets to train and evaluate models. To download them programmatically, you need the kaggle CLI:

pip install kaggle

Go to your Kaggle Account settings page and click on Create new API Token to download the file to be saved as ~/.kaggle/kaggle.json for authentication.

Basketball-Instants-Dataset

The Basketball-Instants-Dataset is a dataset of independant instants (raw images captured at the same instant by the Keemotion automated production system).

The dataset can be downloaded and unzipped manually in the basketball-instants-dataset/ folder of the project, or programmatically with:

kaggle datasets download deepsportradar/basketball-instants-dataset
unzip -qo ./basketball-instants-dataset.zip -d basketball-instants-dataset

Ballistic-Raw-Sequences-Dataset

The Ballistic-Raw-Sequences-Dataset is a dataset of raw sequences captured by the Keemotion production system.

The dataset can be downloaded and unzipped manually in the ballistic-raw-sequences/ folder of the project, or programmatically with:

kaggle datasets download gabrielvanzandycke/ballistic-raw-sequences
unzip -qo ./ballistic-raw-sequences.zip -d ballistic-raw-sequences

Tasks

The tasks are determined by a configuration file (located in the configs folder) that uses several functions and objects defined in the models and tasks folders. The tasks rely on a pre-processed dataset that needs to be computed and stored in your DATA_PATH.

The models can be trained by running the following command from the project root folder (or by adding the project root folder to the DATA_PATH environment variable):

python -m experimentator configs/<config-file> --epochs <numper-of-epochs>

Note: Configuration parameters can be overwritten from the command line by adding --kwargs "<param-name>=<param-value>"

Task name	Configuration file	Dataset generation script	Notebook available
BallSeg	configs/ballseg.py	scripts/prepare_camera_views_dataset.py	yes
PIFBall	configs/pifball.py	scripts/prepare_camera_views_dataset.py	yes
BallSize	configs/ballsize.py	scripts/prepare_ball_views_dataset.py	yes

BallSeg: ball detection with a segmentation approach

This tasks addresses ball detection in basketball scenes. The pre-processed dataset items have the following attributes:

• image: a numpy.ndarray RGB image with ball visible somewhere.
• calib: a calib3d.Calib object describing the calibration data associated to image using the Keemotion convention.
• ball : a Ball object with attributes:
  • center: the ball 3D position as a calib3d.Point3D object (use calib.project_3D_to_2D(ball.center) to retrieve pixel coordinates).
  • visible: a flag telling if ball is visible (always True in this file)

The notebook to load the dataset and run the model training is available here: notebooks/run_ballseg_experiment.ipynb.

PIFBall: ball detection with a Part-Intensity-Field

This tasks addresses ball detection using a keypoint detection approach greatly inspired by PifPaf. The pre-processed dataset is the same than the one from the BallSeg task.

Important note: The computation required for the evaluation phase is extremely long when the model is untrained. For this reasons, evaluation phase should be skipped for the first few epochs:

python -m experimentator configs/pifball.py --epochs 101 --kwargs "eval_epochs=range(20,101,20)"

BallSize: ball diameter estimation for 3D localization

This tasks addresses ball 3D localization from a single calibrated image. The pre-processed dataset items have the following attributes:

• image: a numpy.ndarray RGB image thumbnail centered on the ball.
• calib: a calib3d.Calib object describing the calibration data associated to image using the Keemotion convention.
• ball : a Ball object with attributes:
  • center: the ball 3D position as a calib3d.Point3D object (use calib.project_3D_to_2D(ball.center) to retrieve pixel coordinates).
  • visible: a flag telling if ball is visible.

You can visualize this dataset the following way:

from mlworkflow import PickledDataset
from matplotlib import pyplot as plt
ds = PickledDataset("basketball-instants-dataset/ball_views.pickle")
for key in ds.keys:
    item = ds.query_item(key)
    plt.imshow(item.image)
    plt.title("ball size: {:.1f}".format(item.calib.compute_length2D(23, item.ball.center)[0]))
    plt.show()
    break # avoid looping through all dataset

This task uses the split defined by DeepSportDatasetSplitter which

1. Uses images from KS-FR-CAEN, KS-FR-LIMOGES and KS-FR-ROANNE arenas for the testing-set.
2. Randomly samples 15% of the remaining images for the validation-set
3. Uses the remaining images for the training-set. The testing-set should not be used except to evaluate your model and when communicating about your method.

Ball state classification

To be released

License

This project is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) - visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for more details.

Citation

If you find this work useful and want to cite it in your own research, projects, or publications, please use the following citation.

Please make sure to comply with the terms of the license when using or referencing this work.

If you have any questions about citing the work or need further information, you should contact me.

Datasets

• Initial DeepSport dataset (315 samples):

@inproceedings{VanZandycke2019,
  Author = {Gabriel {Van Zandycke} and Christophe {De Vleeschouwer}},
  Title = {Real-time CNN-based segmentation architecture for ball detection in a single view setup},
  Booktitle = {MMSports 2019 - Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports, co-located with MM 2019},
  Year = {2019}
  DOI = {10.1145/3347318.3355517},
}

• Extended DeepSport dataset (364 samples):

@inproceedings{deepsportradar,
  Author = {Gabriel {Van Zandycke} and Vladimir Somers and Maxime Istasse and Carlo {Del Don} and Davide Zambrano},
  Title = {DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations},
  Booktitle = {Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports},
  Year = {2022},
  DOI = {10.1145/3552437.3555699},
}

• Ball 3D localization over ballistic trajectories (233 samples):

@inproceedings{ball3d,
  Author = {Gabriel {Van Zandycke} and Christophe {De Vleeschouwer}},
  Title = {3D Ball Localization From A Single Calibrated Image},
  Booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  year = {2022}
  DOI = {10.1109/CVPRW56347.2022.00391},
}

Tasks

• Ball detection with a segmentation approach:

@inproceedings{VanZandycke2019,
  Author = {Gabriel {Van Zandycke} and Christophe {De Vleeschouwer}},
  Title = {Real-time CNN-based segmentation architecture for ball detection in a single view setup},
  Booktitle = {MMSports 2019 - Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports, co-located with MM 2019},
  Year = {2019}
  DOI = {10.1145/3347318.3355517},
}

• Ball detection with a keypoint detection approach:

@inproceedings{Ghasemzadeh2021,
  Author = {Abolfazl {S.A. Ghasemzadeh} and Gabriel {Van Zandycke} and Maxime Istasse and Niels Sayez and Amirafshar Moshtaghpour and Christophe {De Vleeschouwer}},
  Booktitle = {32nd British Machine Vision Conference 2021 (BMVC 2021)},
  Title = {DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes},
  Year = {2021}
}

• Ball 3D localization with a diameter approach method:

@inproceedings{ball3d,
  Author = {Gabriel {Van Zandycke} and Christophe {De Vleeschouwer}},
  Title = {3D Ball Localization From A Single Calibrated Image},
  Booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  year = {2022}
  DOI = {10.1109/CVPRW56347.2022.00391},
}

• Ball 3D localization baseline

@inproceedings{deepsportradar,
  Author = {Gabriel {Van Zandycke} and Vladimir Somers and Maxime Istasse and Carlo {Del Don} and Davide Zambrano},
  Title = {DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations},
  Booktitle = {Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports},
  Year = {2022},
  DOI = {10.1145/3552437.3555699},
}

• Ball 3D localization from a single image with a diameter approach:

comming soon