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Normalizing Flows for Human Pose Anomaly Detection [ICCV 2023]
The official PyTorch implementation of the paper "Normalizing Flows for Human Pose Anomaly Detection".
Citation
If you find this useful, please cite this work as follows:
@InProceedings{Hirschorn_2023_ICCV,
author = {Hirschorn, Or and Avidan, Shai},
title = {Normalizing Flows for Human Pose Anomaly Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {13545-13554}
}
Getting Started
This code was tested on Ubuntu 20.04.4 LTS
and requires:
- Python 3.8
- conda3 or miniconda3
- CUDA capable GPU (one is enough)
Setup Conda Environment:
git clone https://github.com/orhir/STG-NF
cd STG-NF
# Conda environment setup
conda env create -f environment.yml
conda activate STG-NF
Directory Structure
.
├── checkpoints
├── data
│ ├── ShanghaiTech
│ │ ├── gt
│ │ │ └── test_frame_mask
│ │ └── pose
│ │ ├── test
│ │ └── train
│ └── UBnormal
│ ├── gt
│ ├── pose
│ │ ├── abnormal_train
│ │ ├── test
│ │ ├── train
│ │ └── validation
│ └── videos
├── models
│ └── STG_NF
└── utils
Data Directory
Data folder, including extracted poses and GT, can be downloaded using the link.
The data directory holds pose graphs and ground truth vectors for the datasets. A path for the directory may be configured using the arguments:
--vid_path_train
--vid_path_test
--pose_path_train
--pose_path_train_abnormal
--pose_path_test
Custom Dataset
We provide a script for creating JSON files in the accepted format using AlphaPose. Please download into pretrained_models folders fast_421_res152_256x192.pth It is also recommended to use the YOLOX-X detector, which can be downloaded from the AlphaPose repository. Use the flag --video for video folder, otherwise assumes a folder of JPG/PNG images for each video. python gen_data.py --alphapose_dir /path/to/AlphaPoseFloder/ --dir /input/dir/ --outdir /output/dir/ [--video]
Training/Testing
Training and Evaluating is run using:
python train_eval.py --dataset [ShanghaiTech\UBnormal]
Evaluation of our pretrained model can be done using:
ShanghaiTech/ShanghaiTech-HR:
python train_eval.py --dataset [ShanghaiTech/ShanghaiTech-HR] --checkpoint checkpoints/ShanghaiTech_85_9.tar
Unsupervised UBnormal
python train_eval.py --dataset UBnormal --seg_len 16 --checkpoint checkpoints/UBnormal_unsupervised_71_8.tar
Supervised UBnormal
python train_eval.py --dataset UBnormal --seg_len 16 --R 10 --checkpoint checkpoints/UBnormal_supervised_79_2.tar
Acknowledgments
Our code is based on code from:
License
This code is distributed under a Creative Commons LICENSE.
Note that our code depends on other libraries and uses datasets that each have their own respective licenses that must also be followed.