Code and Pre-processed data for the paper Pose and Joint-Aware Action Recognition accepted to WACV 2022
- Tested with Python Version : 3.7.11
Follow one of the following to set up the environment:
- A) Install from conda environment :
conda env create -f environment.yml
- B) The code mainly requires the following packages : torch, torchvision, puytorch
- Install one package at a time :
conda create -n pose_action python=3.7
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install opencv-python matplotlib wandb tqdm joblib scipy scikit-learn
- C) Make an account on wandb and make required changes to
train.py
L36
mkdir data
mkdir metadata
- Download data from here. Extract the tar files with folder structure
data/$dataset/openpose_COCO_3/
- Download metadata from here. Extract the tar files to
data/metadata
- Example :
bash sample_scripts/hmdb.sh
We also provide raw heatmaps here. OpenPose was used to extract these. Please take a look at function final_extract_hmdb
in utils.py
for an example function to extract pose data.
If you find this repository useful in your work, please cite us!
@InProceedings{Shah_2022_WACV,
author = {Shah, Anshul and Mishra, Shlok and Bansal, Ankan and Chen, Jun-Cheng and Chellappa, Rama and Shrivastava, Abhinav},
title = {Pose and Joint-Aware Action Recognition},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2022},
pages = {3850-3860}
}