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Spatio-Temporal Dynamic Inference Network for Group Activity Recognition

The source codes for ICCV2021 Paper Spatio-Temporal Dynamic Inference Network for Group Activity Recognition.

Note that we also incorporate the core implementation of our AAAI 2021 paper Learning Visual Context for Group Activity Recognition in this repo.

If you find our work or the codebase inspiring and useful to your research, please consider ⭐starring⭐ the repo and citing:

@inproceedings{yuan2021DIN,
  title={Spatio-Temporal Dynamic Inference Network for Group Activity Recognition},
  author={Yuan, Hangjie and Ni, Dong and Wang, Mang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7476--7485},
  year={2021}
}
@inproceedings{yuan2021visualcontext,
  title={Learning Visual Context for Group Activity Recognition},
  author={Yuan, Hangjie and Ni, Dong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={4},
  pages={3261--3269},
  year={2021}
}

Dependencies

• Software Environment: Linux (CentOS 7)
• Hardware Environment: NVIDIA TITAN RTX
• Python 3.6
• PyTorch 1.2.0, Torchvision 0.4.0
• RoIAlign for Pytorch

Prepare Datasets

1. Download publicly available datasets from following links: Volleyball dataset and Collective Activity dataset.
2. Unzip the dataset file into data/volleyball or data/collective.
3. Download the file tracks_normalized.pkl from cvlab-epfl/social-scene-understanding and put it into data/volleyball/videos

Using Docker

1. Checkout repository and cd PROJECT_PATH

2. Build the Docker container

docker build -t din_gar https://github.com/JacobYuan7/DIN_GAR.git#main

3. Run the Docker container

docker run --shm-size=2G -v data/volleyball:/opt/DIN_GAR/data/volleyball -v result:/opt/DIN_GAR/result --rm -it din_gar

• --shm-size=2G: To prevent ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm)., you have to extend the container's shared memory size. Alternatively: --ipc=host
• -v data/volleyball:/opt/DIN_GAR/data/volleyball: Makes the host's folder data/volleyball available inside the container at /opt/DIN_GAR/data/volleyball
• -v result:/opt/DIN_GAR/result: Makes the host's folder result available inside the container at /opt/DIN_GAR/result
• -it & --rm: Starts the container with an interactive session (PROJECT_PATH is /opt/DIN_GAR) and removes the container after closing the session.
• din_gar the name/tag of the image
• optional: --gpus='"device=7"' restrict the GPU devices the container can access.

Get Started

1. Train the Base Model: Fine-tune the base model for the dataset.

   # Volleyball dataset
   cd PROJECT_PATH 
   python scripts/train_volleyball_stage1.py
   
   # Collective Activity dataset
   cd PROJECT_PATH 
   python scripts/train_collective_stage1.py
   

2. Train with the reasoning module: Append the reasoning modules onto the base model to get a reasoning model.

   1. Volleyball dataset

      • DIN

        python scripts/train_volleyball_stage2_dynamic.py
        

      • lite DIN
        We can run DIN in lite version by setting cfg.lite_dim = 128 in scripts/train_volleyball_stage2_dynamic.py.

        python scripts/train_volleyball_stage2_dynamic.py
        

      • ST-factorized DIN
        We can run ST-factorized DIN by setting cfg.ST_kernel_size = [(1,3),(3,1)] and cfg.hierarchical_inference = True.

        Note that if you set cfg.hierarchical_inference = False, cfg.ST_kernel_size = [(1,3),(3,1)] and cfg.num_DIN = 2, then multiple interaction fields run in parallel.

        python scripts/train_volleyball_stage2_dynamic.py
        

      Other model re-implemented by us according to their papers or publicly available codes:
      • AT

        python scripts/train_volleyball_stage2_at.py
        

      • PCTDM

        python scripts/train_volleyball_stage2_pctdm.py
        

      • SACRF

        python scripts/train_volleyball_stage2_sacrf_biute.py
        

      • ARG

        python scripts/train_volleyball_stage2_arg.py
        

      • HiGCIN

        python scripts/train_volleyball_stage2_higcin.py
        

   2. Collective Activity dataset
      • DIN

        python scripts/train_collective_stage2_dynamic.py
        

      • DIN lite
        We can run DIN in lite version by setting 'cfg.lite_dim = 128' in 'scripts/train_collective_stage2_dynamic.py'.

        python scripts/train_collective_stage2_dynamic.py
        

Another work done by us, solving GAR from the perspective of incorporating visual context, is also available.

@inproceedings{yuan2021visualcontext,
  title={Learning Visual Context for Group Activity Recognition},
  author={Yuan, Hangjie and Ni, Dong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={4},
  pages={3261--3269},
  year={2021}
}