Video anomaly detection with PyTorch

Introduction

This is a PyTorch implementation of an anomaly detection in video using Convolutional LSTM AutoEncoder. This project is inspired by some articles below.

Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, Learning Temporal Regularity in Video Sequences (2016), arXiv:1604.04574.
Yong Shean Chong, Abnormal Event Detection in Videos using Spatiotemporal Autoencoder (2017), arXiv:1701.01546.


Input image	result

Preresquisties

PyTorch
tqdm
Albumentations
NumPy

How to train

For training, you should get a sequence of video frames which contains 'normal' situations.
It is a good way to see UCSD Anomaly Detection Dataset to get more details.
Put a sequence and set your training phase at train.py

class args():
    # Dataset setting
    channels = 3
    size = 256
    frames_dir = 'datasets/train'
    time_steps = 10

where time_steps denotes each of size using the sliding window technique.

Run the command below

python train.py

How to evaluate

Just run the command below

python evaluate.py

Then, you can get regularity scores at each frame.

Notes