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<!DOCTYPE html>
<html lang="">
<head>
<meta charset="UTF-8">
<title>hizuka</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="theme-color" content="#157878">
<link rel="stylesheet" href="css/normalize.css">
<link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
<link rel="stylesheet" href="css/cayman.css">
</head>
<body>
<section class="page-header">
<h1 class="project-name"> Complex Video Shadow Dataset (CVSD) </h1>
<h2 class="project-tagline">196 video clips including 19,757 frames</h2>
<a href="https://github.com/hizuka590/CVSD" class="btn">Github source</a>
<a href="https://drive.google.com/drive/folders/10kWd-mtCf6eYNeaNTMWdLBKoY6S0bqYy?usp=sharing" class="btn">Download from Google Drive</a>
<!-- <a href="https://pan.baidu.com/s/1DYjXERQuIlbtNPe4wFcJXA" class="btn">Download from Baidu Disk (q0lh)</a>-->
</section>
<section class="main-content">
<div id="home">
<!-- <h1>Blog Posts</h1> -->
<ul class="posts">
<!-- <li><span>06 Apr 2021</span> » <a href="/main/2021/04/06/ViSha.html">Welcome to ViSha!</a></li> -->
<!-- <li><span>06 Apr 2021</span> » <a href="/main/2021/04/06/ViSha.html">Welcome to ViSha!</a></li> -->
<h2 id="introduction-for-CVSD">Introduction for CVSD</h2>
<p> To facilitate the exploration of video shadow detection in the wild, we build a new dataset named Complex Video Shadow Dataset (<strong>CVSD</strong>). It comprises 196 video clips featuring diverse scenarios encompassing various shadow patterns across 149 categories, resulting in a wide range of challenging cases and shadow characteristics. Within the dataset, we carefully annotate 309,183 disjoint shadow areas, yielding a collection of 19,757 frames with high-quality shadow masks for training and evaluating video shadow detection methods in real-world and complex scenarios. <br />
Eventually, our <strong>CVSD</strong> enjoys notable features including <strong>complex and diverse shadow patterns, improved resolution and crowded objects, expanded illumination scenarios</strong>, which pose more opportunities and challenges for video shadow detection.</p>
<h2 id="quickly-know-CVSD1">CVSD Dataset in Detail</h2>
<p>
</p>
<p><img src="https://github.com/hizuka590/CVSD/blob/main/dataset_teaser.jpg?raw=true" /></p>
<h2 id="quickly-know-CVSD2">Enhanced resolution and Crowded Objects</h2>
<p>
Previous video shaodw datasets struggle with low resolution and simple scenes, which reduces its ability to detect small or distant shadows in real-world scenes. By comparison, our dataset has been carefully selected to perform exceptionally well in the identification of shadows in real-world circumstances. This is achieved by utilizing its much greater resolution and purposeful inclusion of crowded objects.
We provide comprehensive visual examples of our intricate background with a high object density. One noteworthy feature of our dataset is the addition of densely packed backgrounds, which adds another layer of complexity to shadow detection. Our dataset's applicability to real-world settings is further enhanced by addressing the difficulty of recognizing shadows for small and distant objects, as demonstrated in Column 1, Row 2, where shadows for pedestrians and pigeons in the distance are accurately defined.
Unlike approaches fixated on dominant shadow instances, our dataset distinguishes itself by offering an unprecedented level of detail. This is seen in Column 3, Row 1, where the labels for the shadows on the fence are very detailed. Together, these improvements strengthen our practical utility and robustness in real-world circumstances.
</p>
<p><img src="https://github.com/hizuka590/CVSD/blob/main/dataset_objects.png?raw=true" /></p>
<h2 id="quickly-know-CVSD3">Diverse Shadow Patterns and Illumination Scenarios</h2>
<p>
Our dataset displays a wide range of shadow patterns that are impacted by many circumstances, including different kinds of motion, changes in perspective, and a variety of object and scene kinds. Visual examples below show differences between camera types (such as macro, fish eye, and drone aerial photography). This allows for the introduction of viewpoint shifts, motion blur, and many dynamic views.
</p>
<p><img src="https://github.com/hizuka590/CVSD/blob/main/dataset_camera.png?raw=true" /></p>
<p>
Our CVSD encompasses shadows generated by multiple light sources, thereby extending the range of original illumination scenarios or scene types beyond conventional categories such as indoor, outdoor, day, and night to include 12 distinct types. Specifically, we broaden 'indoor' to encompass stage lighting, bar lighting, and common indoor lighting. The category of 'night' lighting is expanded to include spotlights and floodlights. Similarly, 'day' lighting is extended to include sunrise, dusk, overcast, and sunny variations. Additionally, the 'outdoor' category is expanded to urban, waterfront, and natural types. This expansion is driven by our observation that even within a single indoor lighting category, bar lighting provides softer shadows because the light is not very bright, while stage lighting provides sharp shadows but the light switches very frequently. Shadows caused by these different factors will have different patterns, and with the new scenarios we have introduced, the trained model will be more robust in practice.
</p>
<p><img src="https://github.com/hizuka590/CVSD/blob/main/dataset_light.png?raw=true" /></p>
<!--<h2 id="form-of-CVSD">Form of CVSD</h2>-->
<!--<p>To provide guidelines for future works, we randomly split the dataset into training and testing sets with a ratio of 5:7. The 50 training set and 70 testing set can be downloaded <em>above this page</em>.</p>-->
<!--<p>If you download CVSD and unzip each file, you can find the dataset structure as follows:</p>-->
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>▾ <train>/
▾ images/
▾ 000/
0000.jpg
0001.jpg
...
▾ trees/
0000.jpg
...
...
▾ labels/
▾ 000/
0000.png
0001.png
...
▾ trees/
0000.png
...
...
▾ <test>/
...
</code></pre></div></div>
<!-- <img src="https://github.com/eraserNut/eraserNut.github.io/blob/main/_posts/folder_structure.png?raw=true" height="500"> -->
<h2 id="statistics-of-CVSD">Statistics of CVSD</h2>
<p><strong>CVSD include many shadow attributes:</strong></p>
<!--<p><img src="https://github.com/hizuka590/CVSD/blob/main/dataset_camera.png?raw=true" /></p>-->
<p><img src="https://github.com/hizuka590/CVSD/blob/main/stat.png?raw=true"width="730" /></p>
<!--<p><strong>Visualization of the statistics of CVSD.</strong> (a) Shadow categories. (b) Ratio distribution of the shadows. (c) Mutual dependencies among shadow categories in (a).</p>-->
<!--<p><img src="https://github.com/eraserNut/eraserNut.github.io/blob/main/_posts/visha_figure.png?raw=true" width="700" /></p>-->
<!--<h2 id="citation">Citation</h2>-->
<!--<p><strong>If you utilize CVSD in your work, please cite our paper as follows</strong></p>-->
<!--<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>-->
<!-- @InProceedings{Duan_2024_ECCV,-->
<!--author = {Zhu, Lei and Deng, Zijun and Hu, Xiaowei and Fu, Chi-Wing and Xu, Xuemiao and Qin, Jing and Heng, Pheng-Ann},-->
<!--title = {Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection},-->
<!--booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},-->
<!--month = {September},-->
<!--year = {2018}-->
<!--}-->
<!--}-->
<!--</code></pre></div></div>-->
<!--<p>Paper arXiv link: https://arxiv.org/abs/2103.06533</p>-->
</ul>
</div>
<footer class="site-footer">
<!-- <span class="site-footer-owner">CVSD is maintained by Visual Intelligence Lab, College of Intelligence and Computing, Tianjin university.</span>-->
<span class="site-footer-credits">You can email us to ask more about CVSD: [email protected] </span>
</footer>
</section>
</body>
</html>