Intro

December 2, 2025 ยท View on GitHub

Our codes are based on https://github.com/JacobYuan7/DIN-Group-Activity-Recognition-Benchmark.
I deeply appreciate their efforts. This is the official repository for the following paper:

Chihiro Nakatani, Hiroaki Kawashima, Norimichi Ukita
Learning Group Activity Features Through Person Attribute Prediction, CVPR2024
Project page: https://toyota-ti.ac.jp/Lab/Denshi/iim/ukita/selection/CVPR2024-GAFL.html

Top page

Citation

@inproceedings{DBLP:conf/cvpr/NakataniKU24,
  author       = {Chihiro Nakatani and
                  Hiroaki Kawashima and
                  Norimichi Ukita},
  title        = {Learning Group Activity Features Through Person Attribute Prediction},
  booktitle    = {CVPR},
  year         = {2024},
}

Environment

python 3.10.2
ROIAlign (https://github.com/longcw/RoIAlign.pytorch)

And you can use requirements.txt

pip install -r requirements.txt

Data preparation

1. Download dataset

You can download daatset from the following url.
These dataset are required to place in data/ in the repository as follows:

2. Training

2.1 Volleyball dataset

  • Ours
python scripts/train_volleyball_stage2_gr.py

The following folder contains the trained models.

  1. GAFL_PAC_VOL (GAFL-PAC)
  2. GAFL_PAF_VOL (GAFL-PAF)

2.2 Collective Activity dataset

  • Ours
python scripts/train_collective_stage2_gr.py

The following folder contains the trained models.

  1. GAFL_PAC_CAD (GAFL-PAC)
  2. GAFL_PAF_CAD (GAFL-PAF)

3. Evaluation

3.1 Volleyball dataset

You can choose the model that you would like to evaluate in the bash file script.

  • Ours
bash ./evaluation_vol.bash

3.2 Collective Activity dataset

  • Ours
bash ./evaluation_cad.bash