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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>HEADS-UP: Head-Mounted Egocentric Dataset for Trajectory Prediction in Blind Assistance Systems</title>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
<link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css" rel="stylesheet">
<link rel="icon" href="./static/images/favicon.svg">
<style>
.image-container {
display: grid; /* Arrange images in a grid */
grid-template-columns: repeat(2, 1fr); /* Two images per row */
gap: 10px; /* Add spacing between images */
justify-content: center; /* Center the grid */
margin-top: 20px; /* Add spacing from the top */
}
.video-container {
display: grid;
grid-template-columns: repeat(2, 1fr); /* Four equal columns for videos */
gap: 10px; /* Space between items */
margin: 20px 0; /* Add vertical spacing between sections */
align-items: stretch; /* Ensure items fill the grid cell vertically */
justify-items: stretch; /* Ensure items fill the grid cell horizontally */
}
.video-item {
display: flex;
flex-direction: column;
align-items: center; /* Center align descriptions and videos */
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.video-description {
margin-bottom: 10px;
font-weight: bold; /* Make the description stand out */
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.section-description {
margin-bottom: 30px;
font-size: 1.2em;
color: #555; /* Subtle color to differentiate from headings */
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.content {
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.pseudo-video-container video {
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object-fit: cover; /* Ensures the video content scales nicely */
}
</style>
</head>
<body>
<div style="background-color: #e6ffe6; padding: 10px; text-align: center; color: green; font-weight: bold;">
We recommend using Chrome for the best experience. Videos might not work on Safari.
</div>
<section class="hero title-section" style="background-color: #f0f0f0;">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1">
HEADS-UP <br>
<span style="font-size: 0.8em; font-weight: normal;">
Head-Mounted Egocentric Dataset for Trajectory Prediction in Blind Assistance Systems
</span>
</h1>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Paper Link -->
<span class="link-block">
<a href="https://arxiv.org/abs/2409.20324"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Dataset Link -->
<span class="link-block">
<a href="https://huggingface.co/datasets/Yassaman/HEADS-UP"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="far fa-images"></i>
</span>
<span>Dataset</span>
</a>
</span>
<!-- GitHub Link -->
<span class="link-block">
<a href="https://github.com/vita-epfl/HEADS-UP"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
<strong>HEADS-UP</strong> is the first egocentric dataset collected from head-mounted cameras, designed specifically for trajectory prediction in blind assistance systems.
Given the large population of blind and visually impaired individuals, there is an increasing need for intelligent assistive tools that can provide real-time warnings about potential collisions with dynamic obstacles.
These systems depend on algorithms that can predict the trajectories of moving objects, such as pedestrians, to issue timely hazard alerts.
However, current datasets do not capture the essential information from the perspective of a blind individual.
To bridge this gap, we introduce <strong>HEADS-UP</strong>, a novel dataset specifically designed for trajectory prediction in this context.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container has-text-centered">
<h2 class="title is-2">Examples</h2>
</p>
<div class="video-container">
<video controls loop autoplay muted>
<source src="./static/videos/video1.MP4.mov" type="video/mp4">
</video>
<video controls loop autoplay muted>
<source src="./static/videos/video2.MP4.mov" type="video/mp4">
</video>
<video controls loop autoplay muted>
<source src="./static/videos/video3.MP4.mov" type="video/mp4">
</video>
<video controls loop autoplay muted>
<source src="./static/videos/video4.MP4.mov" type="video/mp4">
</video>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{haghighi2024heads,
title={HEADS-UP: Head-Mounted Egocentric Dataset for Trajectory Prediction in Blind Assistance Systems},
author={Haghighi, Yasaman and Demonsant, Celine and Chalimourdas, Panagiotis and Naeini, Maryam Tavasoli and Munoz, Jhon Kevin and Bacca, Bladimir and Suter, Silvan and Gani, Matthieu and Alahi, Alexandre},
journal={arXiv preprint arXiv:2409.20324},
year={2024}
}</code></pre>
</div>
</section>
</body>
</html>