Self-Supervised Sparse Representation for Video Anomaly Detection

x By Jhih-Ciang Wu*, He-Yen Hsieh*, Ding-Jie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu (The symbol of * denotes equal contribution)

tags: `video anomaly detection` `weakly-supervised` `dictionary learning`

This repo is the official implementation of "Self-Supervised Sparse Representation for Video Anomaly Detection" (accepted at ECCV'22) for the weakly-supervised VAD (wVAD) setting.

Table of Contents

0. Introduction
1. Quick start
2. Prerequisitesn
3. Installation
4. Data preparation
5. Dictionary learning
6. Results and Models
7. Evaluation
8. Training
9. Acknowledgement
10. Citation

Introduction

We consider establishing a dictionary learning approach to model the concept of anomaly at the feature level. The dictionary learning presumes an overcomplete basis, and prefers a sparse representation to succinctly explain a given sample. With the training set $\mathcal{X}$, whose video samples are anomaly-free, we are motivated to learn its corresponding dictionary $D$ of $N$ atoms. Since the derivation of $D$ is specific to the training dataset $\mathcal{X}$, we will use the notation $D_T$ to emphasize that the underlying dictionary is task-specific. With the learned task-specific dictionary $D_T$, we can design two opposite network components: the en-Normal and de-Normal modules. Given a snippet-level feature $F$, the former is used to obtain its reconstructed normal-event feature, while, on the contrary, the latter is applied to filter out the normal-event feature. The two modules complement each other and are central to our approach to anomaly video detection.

Quick start

# please refer to the "Installation" section
$ conda create --name s3r python=3.6 -y
$ conda activate s3r
$ conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
$ cd S3R/
$ pip install -r requirements.txt

# please refer to the "Data preparation" section
$ ln -sT <your-data-path>/SH_Train_ten_crop_i3d data/shanghaitech/i3d/train
$ ln -sT <your-data-path>/SH_Test_ten_crop_i3d data/shanghaitech/i3d/test

# please refer to the "Dictionary learning" section
$ ln -sT <downloaded-dictionary-path>/ dictionary

# please refer to the "Evaluation" section
$ CUDA_VISIBLE_DEVICES=0 python tools/trainval_anomaly_detector.py \
--dataset shanghaitech --inference --resume checkpoint/shanghaitech_s3r_i3d_best.pth

Prerequisites

Operating system
- Ubuntu 18.04.6 LTS
Graphics card
- GPU: NVIDIA RTX 2080 Ti
Framework and environment
- pytorch: 1.6.0
- cuda: 10.1
- torchvision: 0.7.0
Programming language
- python: 3.6

Library versions for reference

The following information denotes the versions of installed libraries in our experiments.

Library versions
- pyyaml==6.0
- tqdm==4.64.0
- munch==2.5.0
- terminaltables==3.1.0
- scikit-learn==0.24.2
- opencv-python==4.6.0
- pandas==1.1.5
- typed-argument-parser==1.7.2
- einops==0.4.1

Project structure

$ tree S3R
S3R/
├─ anomaly/    (directory for core functions, including dataloader, S3R modules, and other useful functions)
├─ checkpoint/ (directory for model weights)
├─ configs/    (directory for model configurations)
├─ data/       (directory for dataset)
├─ dictionary/ (directory for learned dictionaries)
├─ tools/      (directory for main scripts)
├─ logs/       (directory for saving training logs)
├─ output/     (directory for saving inference results)
├─ config.py
├─ README.md 
├─ requirements.txt 
├─ utils.py

Installation

Step 1. Create a conda environment and activate it.

$ conda create --name s3r python=3.6 -y
$ conda activate s3r

Step 2. Install pytorch

$ conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
or
$ pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html

Step 3. Install required libraries

$ pip install -r requirements.txt

Data preparation

Please download the extracted I3d features for shanghaitech and ucf-crime dataset from the link.

The file structure of downloaded features should look like:

$ tree data
data/
├─ shanghaitech/
│  ├─ shanghaitech.training.csv
│  ├─ shanghaitech_ground_truth.testing.json
│  ├─ shanghaitech.testing.csv
│  ├─ i3d/
│  │  ├─ test/
│  │  │  ├─01_0015_i3d.npy
│  │  │  ├─05_033_i3d.npy
│  │  │  ├─ ...
│  │  ├─ train/
│  │  │  ├─ 01_0014_i3d.npy
│  │  │  ├─ 05_040_i3d.npy
│  │  │  ├─ ...
├─ ucf-crime/
│  ├─ ucf-crime_ground_truth.testing.json
│  ├─ ucf-crime.testing.csv
│  ├─ ucf-crime.training.csv
│  ├─ i3d/
│  │  ├─ test/
│  │  │  ├─ Abuse028_x264_i3d.npy
│  │  │  ├─ Burglary079_x264_i3d.npy
│  │  │  ├─ ...
│  │  ├─ train/
│  │  │  ├─ Abuse001_x264_i3d.npy
│  │  │  ├─ Burglary001_x264_i3d.npy
│  │  │  ├─ ...

Examples:

$ ln -sT <your-data-path>/SH_Train_ten_crop_i3d data/shanghaitech/i3d/train
$ ln -sT <your-data-path>/SH_Test_ten_crop_i3d data/shanghaitech/i3d/test
$ ln -sT <your-data-path>/UCF_Train_ten_crop_i3d data/ucf-crime/i3d/train
$ ln -sT <your-data-path>/UCF_Test_ten_crop_i3d data/ucf-crime/i3d/test

Dictionary learning

The dictionaries can be downloaded from the link and the file structure of dictionaries should look like:

$ tree dictionary
dictionary/
├─ kinetics400
│  ├─ kinetics400_dictionaries.universal.omp.100iters.npy
├─ shanghaitech
│  ├─ shanghaitech_dictionaries.taskaware.omp.100iters.90pct.npy
│  ├─ shanghaitech_regular_features-2048dim.training.pickle
├─ ucf-crime
│  ├─ ucf-crime_dictionaries.taskaware.omp.100iters.50pct.npy
│  ├─ ucf-crime_regular_features-2048dim.training.pickle

Example:

$ ln -sT <downloaded-dictionary-path>/ dictionary

(Optional) Generate dictionaries

To generate dictionaries for the shanghaitech and ucf-crime dataset, please run the following commands:

# for the shanghaitech dataset
$ python data/shanghaitech/shanghaitech_dictionary_learning.py
and
# for the ucf-crime dataset
$ python data/ucf-crime/ucf_crime_dictionary_learning.py

Results and Models

config	dataset	backbone	gpus	AUC (%)	ckpt	log
shanghaitech_dl	shanghaitech	I3D	1	97.40	model	log
ucf_crime_dl	ucf-crime	I3D	1	85.99	model	log

Evaluation

To evaluate the S3R on shanghaitech, please run the following command:

$ CUDA_VISIBLE_DEVICES=0 python tools/trainval_anomaly_detector.py \
--dataset shanghaitech --inference --resume checkpoint/shanghaitech_s3r_i3d_best.pth

+ Performance on shanghaitech ----+---------+
|   Dataset    | Method | Feature | AUC (%) |
+--------------+--------+---------+---------+
| shanghaitech |  S3R   |   I3D   |  97.395 |
+--------------+--------+---------+---------+

To evaluate the S3R on ucf-crime, please run the following command:

$ CUDA_VISIBLE_DEVICES=0 python tools/trainval_anomaly_detector.py \
--dataset ucf-crime --inference --resume checkpoint/ucf-crime_s3r_i3d_best.pth

+ Performance on ucf-crime ----+---------+
|  Dataset  | Method | Feature | AUC (%) |
+-----------+--------+---------+---------+
| ucf-crime |  S3R   |   I3D   |  85.989 |
+-----------+--------+---------+---------+

Training

shanghaitech dataset

To train the S3R from scratch on shanghaitech, please run the following command:

$ CUDA_VISIBLE_DEVICES=<gpu-id> python tools/trainval_anomaly_detector.py \
--dataset shanghaitech --version <customized-version> --evaluate_min_step 5000

Example:

$ CUDA_VISIBLE_DEVICES=0 python tools/trainval_anomaly_detector.py \
--dataset shanghaitech --version s3r-vad-0.1 --evaluate_min_step 5000

ucf-crime dataset

To train the S3R from scratch on ucf-crime, please run the following command:

$ CUDA_VISIBLE_DEVICES=<gpu-id> python tools/trainval_anomaly_detector.py \
--dataset ucf-crime --version <customized-version> --evaluate_min_step 10

Example:

$ CUDA_VISIBLE_DEVICES=0 python tools/trainval_anomaly_detector.py \
--dataset ucf-crime --version s3r-vad-0.1 --evaluate_min_step 10

Acknowledgement

Our codebase is built based on RTFM. We really appreciate the authors for the nicely organized code!

Citation

We hope the codebase is beneficial to you. If this repo works positively for your research, please consider citing our paper. Thank you for your time and consideration.

@inproceedings{WuHCFL22,
  author    = {Jhih-Ciang Wu and
               He-Yen Hsieh and
               Ding-Jie Chen and
               Chiou-Shann Fuh and
               Tyng-Luh Liu},
  title     = {Self-Supervised Sparse Representation for Video Anomaly Detection},
  booktitle = {ECCV},
  year      = {2022},
}

louisyen / s3r Goto Github PK

s3r's Introduction

Self-Supervised Sparse Representation for Video Anomaly Detection

tags: video anomaly detection weakly-supervised dictionary learning

Introduction

Quick start

Prerequisites

Library versions for reference

Project structure

Installation

Data preparation

Dictionary learning

(Optional) Generate dictionaries

Results and Models

Evaluation

Training

shanghaitech dataset

ucf-crime dataset

Acknowledgement

Citation

s3r's People

Contributors

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Watchers

Forkers

s3r's Issues

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tags: `video anomaly detection` `weakly-supervised` `dictionary learning`