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Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection

The official PyTorch implementation of the paper "Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection".

Getting Started

This code was tested on Ubuntu 18.04.6 LTS and requires:

• Python 3.8
• conda3 or miniconda3
• CUDA capable GPU

Setup Conda Environment:

git clone https://github.com/benedictstar/Joint-VAD
cd Joint-VAD

# Conda environment setup
conda env create -f environment.yml
conda activate Joint

Data Preparation

Since our work combines two open-source models STG-NF and RTFM, you should organize the data following the guidance of each original repository.

• Pose data can be downloaded from this link (The original pose data comes from STG-NF, and we have repartitioned the dataset to to accommodate the unsupervised mode) and put them into data/weakly_[dataset]/pose.
• I3D features for ShanghaiTech can be downloaded from this link in RTFM.
• I3D features for UBnormal can be downloaded from this link
• Pretrained models can be downloaded from this link and put them into pretrained_models/. (Some results can't match the results in our paper because the original pretrained weights are missing. We provide better results for reference.)

The following files need to be adapted in order to run the code on your own machine:

• Change the file paths to the download datasets above in data/weakly_[dataset]/list/[dataset]-i3d-test-10crop.list and data/weakly_[dataset]/list/[dataset]-i3d-train-10crop.list.

Data Folder Structure

.
├── weakly_ShanghaiTech
│   ├── gt
│   │   └── test_frame_mask
│   ├── list
│   │   ├── gt-sh.npy
│   │   ├── make_gt_sh.py
│   │   ├── make_list_sh.py
│   │   ├── shanghai-i3d-test-10crop.list
│   │   ├── shanghai-i3d-train-10crop.list
│   │   └── test_frame_mask
│   ├── pose
│   │   ├── test
│   │   └── train
│   ├── shanghai-num.npy
│   └── SH_Train.txt
└── weakly_UBnormal
    ├── gt
    │   └── test_gt
    ├── list
    │   ├── gt-ub.npy
    │   ├── make_gt_ub.py
    │   ├── make_list_ub.py
    │   ├── ubnormal-i3d-test-10crop.list
    │   ├── ubnormal-i3d-train-10crop.list
    │   └── ubnormal-i3d-validation-10crop.list
    ├── pose
    │   ├── abnormal_train
    │   ├── test
    │   ├── train
    │   └── validation
    ├── ub-num.npy
    └── UB_Train.txt

Training/Testing

Training and Evaluating is run using:

python main.py --dataset [ShanghaiTech\UBnormal] --ab_ratio [0.15\0.3] --seg_len [24\16]

Evaluation of our pretrained model can be done using:

STG-NF Model for ShanghaiTech:

python OCC_eval.py --dataset ShanghaiTech --Occ_checkpoint pretrained_models/SH_Occ_best_auc_in_UVAD_82.57.pth.tar

RTFM Model for ShanghaiTech:

python WS_eval.py --dataset ShanghaiTech --Weakly_pretrained_ckpt pretrained_models/SH_WS_best_auc_in_UVAD_88.52.pkl

STG-NF Model for UBnormal:

python OCC_eval.py --dataset UBnormal --seg_len 16 --Occ_checkpoint pretrained_models/UB_Occ_best_auc_in_UVAD_74.82.pth.tar

RTFM Model for UBnormal:

python WS_eval.py --dataset UBnormal --Weakly_pretrained_ckpt pretrained_models/UB_WS_best_auc_in_UVAD_63.26.pkl

Acknowledgments

Our code is based on code from:

• STG-NF
• RTFM