Awesome

Framework for Action Recognition and Action Recognition in the Dark

This repository contains the framework for Action Recognition in the Dark.

Prerequisites

This code is based on PyTorch, you may need to install the following packages:

PyTorch >= 0.4 (perfer 1.2 and above)
opencv-python (pip install)
PILLOW (pip install) (optional for optical flow)
scikit-video (optional for optical flow)

Training

Train with initialization from pre-trained models:

python train_arid11.py --network <Network Name> --is-dark

There are a number of parameters that can be further tuned. We recommend a batch size of 16 per GPU. We provide several networks that can be utilized, and can be found in the /network folder, change the --network parameter to toggle through the networks

Testing

Evaluate the trained model:

cd test
python evaluate_video.py

If models with optical flow is used, the following command is used instead:

cd test
python evaluate_flow.py

Other Information

• To download the dataset, please write to xuyu0014@e.ntu.edu.sg for the download link. Thank you! [Update!]
• To view our paper, go to this arxiv link
• If you find our paper useful, please cite our paper:

@article{xu2020arid,
  title={ARID: A New Dataset for Recognizing Action in the Dark},
  author={Xu, Yuecong and Yang, Jianfei and Cao, Haozhi and Mao, Kezhi and Yin, Jianxiong and See, Simon},
  journal={arXiv preprint arXiv:2006.03876},
  year={2020}
}

• Our code base is adapted from Multi-Fiber Network for Video Recognition, we would like to thank the authors for providing the code base.
• You may contact me through xuyu0014@e.ntu.edu.sg
• This work is licensed under a Creative Commons Attribution 4.0 International License.