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Task3-ReplayGrounding

README.md

SoccerNet-v2 - Replay Grounding

This repository contains the code to reproduce the replay grounding result of the paper: "SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos" with two three baslines:

CALF method adapted to the 17 classes of SoccerNet-v2. The SoccerNet-v2 paper can be found here: SoccerNet-v2 paper, and the CALF paper "A Context-Aware Loss Function for Action Spotting in Soccer Videos" here: CALF paper.

@InProceedings{Deliege2020SoccerNetv2,
  author = { Deliège, Adrien and Cioppa, Anthony and Giancola, Silvio and Seikavandi, Meisam J. and Dueholm, Jacob V. and Nasrollahi, Kamal and Ghanem, Bernard and Moeslund, Thomas B. and Van Droogenbroeck, Marc},
  title = {SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos},
  booktitle = {CoRR},
  month = {Nov},
  year = {2020}
}

The task consists in retrieving the timestamp of the action shown in a given replay shot within the whole game.

Getting Started

The following instructions will help you install the required libraries and the dataset to run the code. The code runs in python 3 and was tested in a conda environment. Pytorch is used as deep learning library.

Create environment

To create and setup the conda environment, simply follow the following steps:

conda create -n CALF-pytorch python=3.8
conda activate CALF-pytorch
conda install pytorch=1.6 torchvision=0.7 cudatoolkit=10.1 -c pytorch
pip install SoccerNet

Baselines

  • SoccerNetv2-ReplayGrounding-CALF [Link]
  • SoccerNetv2-ReplayGrounding-CALF_more_negative [Link]
  • SoccerNetv2-ReplayGrounding-NetVLAD-More-Negative [Link]

Evluation on challenge set

Since Replay grounding task is retrieving the timestamp of the action shown in a given replay shot within the whole game, we need to know the start and end points of each replay. Hence, we provided auxiliary annotations for challenge set to address this issue. You can download it using this [Link] and unzip the file in the same directory that you have other labels and features

Authors

  • Anthony Cioppa, University of Liège (ULiège).
  • Adrien Deliège, University of Liège (ULiège).
  • Silvio Giancola, King Abdullah University of Science and Technology (KAUST).
  • Meisam J. Seikavandi, Aalborg University (AAU).
  • Jacob V. Dueholm, Aalborg University (AAU).

See the AUTHORS file for details.

License

Apache v2.0 See the LICENSE file for details.

Acknowledgments

  • Anthony Cioppa is funded by the FRIA, Belgium.
  • This work is supported by the DeepSport project of the Walloon Region, at the University of Liège (ULiège), Belgium.
  • This work is also supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR).
  • This work is also supported by the Milestone Research Program at Aalborg University.