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<!DOCTYPE html>
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content="JDT3D is a novel LiDAR-based tracking method that addresses the performance gap between tracking-by-detection (TBD) and tracking-by-attention (TBA) methods. It is designed for multi-object tracking in autonomous driving.">
<meta name="keywords" content="Multi-Object Tracking, Autonomous Vehicles, Computer Vision">
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<title>JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention</title>
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<h1 class="title is-1 publication-title">JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.trailab.utias.utoronto.ca/brian-cheong">Brian Cheong</a>,</span>
<span class="author-block">
<a href="https://www.trailab.utias.utoronto.ca/jason-zhou">Jiachen Zhou</a>,</span>
<span class="author-block">
<a href="https://www.trailab.utias.utoronto.ca/steven-waslander">Steven Waslander</a>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">University of Toronto</span>
</div>
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<span>arXiv</span>
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</section>
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<img src="./static/images/JDT3D_Architecture_v6-1.png" style="width: 80%;" alt="JDT3D Architecture">
</div>
<h2 class="subtitle has-text-centered">
<span class="dnerf">JDT3D</span> is a novel LiDAR-based tracking method designed for end-to-end multi-object
tracking in autonomous driving.
</h2>
</div>
</div>
</section>
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<h2 class="title is-3">Key Features</h2>
<div class="content has-text-justified">
<ul>
<li><strong>Tracking-by-Attention (TBA)</strong>: JDT3D uses a TBA approach, which represents objects as
vector embeddings or "queries" to detect them across multiple frames.
<li><strong>Joint Detection and Tracking</strong> (JDT): The model performs detection and tracking jointly
in an end-to-end manner, enabling the exchange of information between the detector and tracker.
<li><strong>Track Sampling Augmentation:</strong> JDT3D employs a novel data augmentation method that
injects consistent objects over multiple LiDAR frames to enrich supervision signals while maintaining
temporal consistency.
<li><strong>Confidence-based Query Propagation</strong>: The model uses a confidence threshold for query
propagation during both training and inference to prevent over-trusting false positive queries.
</ul>
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</div>
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<h2 class="title is-3">Introduction</h2>
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<!-- <embed src="./static/images/simplified_JDT3D.pdf" style="width: 100%;" alt="Simplified_TBA"> -->
<img src="./static/images/simplified_JDT3D-1.png" style="width: 100%;" alt="Simplified_TBA">
<p>
Tracking-by-attention performs online multi-object tracking by representing unique objects with
embeddings or "queries" that are propagated across frames. These object queries are passed between time
steps
and used to detect objects in the scene. Queries that output high confidence predictions in multiple
frames
are considered to be associated with the same object.
</p>
<p>
This approach has shown promising results
in the 2D and vision-based tracking domains. However, the performance of TBA methods in the 3D LiDAR
tracking domain has yet to match that of tracking-by-detection (TBD) methods. JDT3D explores this
performance
gap, proposing a novel LiDAR-based tracking method that addresses the limitations of existing LiDAR-based
TBA methods.
</p>
</div>
<h2 class="title is-3">Track Sampling Augmentation</h2>
<div class="content has-text-justified">
<!-- <embed src="./static/images/TrackSampling_Vis_63_with_caption.pdf" style="width: 100%;" alt="Track_Sampling_Augmentation"> -->
<img src="./static/images/TrackSampling_Vis_63_with_caption-1.png" style="width: 100%;" alt="Track_Sampling_Augmentation">
<p>
JDT3D introduces a novel data augmentation method called Track Sampling Augmentation (TSA) to enrich the
supervision
signals for the model. TSA injects consistent objects over multiple LiDAR frames to maintain temporal
consistency while
providing additional supervision signals for the model. This method enables the model to learn from a more
diverse set
of object configurations and improves the robustness of the model to occlusions and object interactions.
</p>
</div>
</div>
</div>
</section>
<section class="section">
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<h2 class="title is-3">Visualization</h2>
<div class="content has-text-justified">
<!-- <embed src="./static/images/jdt3d_qualitative_result.pdf" style="width: 100%;" alt="JDT3D Visualization"> -->
<img src="./static/images/jdt3d_qualitative_result-1.png" style="width: 100%;" alt="JDT3D Visualization">
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</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{cheongJDT3DAddressingGaps2024,
author = {Cheong, Brian and Zhou, Jiachen and Waslander, Steven},
title = {JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention},
journal = {ECCV},
year = {2024},
}</code></pre>
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