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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
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<title>ASTNet</title>
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<body>
<div class="container" id="main">
<div class="row">
<h1 class="col-md-12 text-center publication-title">
Attention-based Residual Autoencoder for Video Anomaly Detection
</h1>
<h2 class="text-center">
<small>
<a href="https://www.springer.com/journal/10489/">
Applied Intelligence
</a>
</small>
</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline">
<li>
<a href="https://vt-le.github.io/">
Viet-Tuan Le
</a>
</li>
<li>
<a href="http://home.sejong.ac.kr/~ykim/">
Yong-Guk Kim
</a>
</li>
</ul>
<ul class="list-inline">
<li>
Department of Computer Sciences and Engineering
</li>
</ul>
<ul class="list-inline">
<li>
Sejong University, Seoul, Korea
</li>
</ul>
</div>
</div>
<div class="row">
<!--<div class="row justify-content-center">-->
<div class="col-md-8 col-sm-8 col-8 col-md-offset-2 col-sm-offset-2 text-center">
<!--<div class="col-12">-->
<ul class="nav nav-pills nav-justified">
<li></li>
<li>
<a href="http://dx.doi.org/10.1007/s10489-022-03613-1">
<image src="static/img/paper_image.PNG" height="60px">
<h4><strong>Paper</strong></h4>
</a>
</li>
<li>
<a href="https://youtu.be/XOzXwKVKX-Y" target="_blank">
<image src="static/img/youtube_icon.png" height="60px">
<h4><strong>Video</strong></h4>
</a>
</li>
<li>
<a href="https://github.com/vt-le/astnet">
<image src="static/img/github.png" height="60px">
<h4><strong>Code</strong></h4>
</a>
</li>
<li></li>
</ul>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<div class="text-center">
<div >
<a href="static/img/shanghai/shanghai_curve_full.gif" target="_blank">
<image style="border: 2px solid rgb(201, 196, 196);" src="static/img/shanghai/shanghai_curve.gif" width="100%">
</a>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3 class="thick">
Abstract
</h3>
<!-- <image src="img/rays.jpg" class="img-responsive" alt="overview"><br> -->
<p class="text-justify">
Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal information effectively. The network has a residual autoencoder architecture, consisting of a Resnet-based encoder and a multi-stage channel attention-based decoder, trained in an unsupervised manner. The residual temporal shift is used for exploiting the temporal feature, whereas the contextual dependency is extracted by channel attention modules. System performance is evaluated using three standard benchmark datasets. Result suggests that our network outperforms the state-of-the-art methods, achieving 97.4% for USCD Ped2, 86.7% for CUHK Avenue, and 73.6% for ShanghaiTech dataset in term of Area Under Curve, respectively.
</p>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3 class="thick">
Method
</h3>
<image src="static/img/astnet_architecture.png" class="img-responsive" alt="overview"><br>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3 class="thick">
Video
</h3>
<div class="text-center">
<div style="position:relative;padding-top:56.25%;">
<!--<iframe src="https://www.youtube.com/embed/_8TTVoYahA8" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>-->
<iframe src="https://www.youtube.com/embed/XOzXwKVKX-Y" title="ASTNet" frameborder="1" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2" id="demos">
<h3 class="thick">
Anomaly Score
</h3>
<h3>
Ped2
</h3>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2 text-center">
<ul class="nav nav-pills nav-justified">
<a href="static/img/ped2/ped2_curve.png" target="_blank">
<image src="static/img/ped2/ped2_curve.png" width="100%">
<h4><strong>Anomaly scores of Video 2 in UCSD Ped2 Dataset</strong></h4>
</a>
</ul>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2" id="demos">
<h3>
Avenue
</h3>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2 text-center">
<ul class="nav nav-pills nav-justified">
<a href="static/img/avenue/ave_curve.png" target="_blank">
<image src="static/img/avenue/ave_curve.png" width="100%">
<h4><strong>Anomaly scores of Video 2 in CUHK Avenue Dataset</strong></h4>
</a>
</ul>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2" id="demos">
<h3>
ShanghaiTech
</h3>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2 text-center">
<ul class="nav nav-pills nav-justified">
<a href="static/img/shanghai/sha_curve.png" target="_blank">
<image src="static/img/shanghai/sha_curve.png" width="100%">
<h4><strong>Anomaly scores of Video 01_0063 in ShanghaiTech Dataset</strong></h4>
</a>
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<h4><strong>Frame 2</strong></h4>
</a>
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<h4><strong>Frame 3</strong></h4>
</a>
</li>
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<h4><strong>Frame 4</strong></h4>
</a>
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</ul>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3 class="thick">
Related Works
</h3>
<ul>
<li>
<a href="https://vt-le.github.io/HSTforU/">
HSTforU: Anomaly Detection in Aerial and Ground-based Videos with Hierarchical Spatio-Temporal Transformer for U-net
</a>
</li>
<li>
<a href="https://moguprediction.github.io/">
MoGuP:Motion-guided Prediction for Video Anomaly Detection
</a>
</li>
</ul>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3 class="thick">
BibTeX
</h3>
<section class="section" id="BibTeX">
<pre><code>@article{le2023attention,
author = {Le, Viet-Tuan and Kim, Yong-Guk},
title = {Attention-based Residual Autoencoder for Video Anomaly Detection},
journal = {Applied Intelligence},
volume = {53},
number = {3},
pages = {3240--3254},
year = {2023},
publisher = {Springer}
}</code></pre>
</section>
</div>
</div>
<!--
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">Citation</h2>
<pre>
@article{le2021a,
title={A Deep and Efficient Spatial-temporal Network for Video Anomaly Detection},
author={Tuan Le-Viet and Yong-Guk Kim},
journal={arXiv},
year={2021}
}
</pre>
</div>
</section>
-->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3 class="thick">
Acknowledgements
</h3>
<p class="text-justify">
This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP), grant funded by the Korea government (MSIT) (No.2019-0-00231).
<br><br>
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