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<meta name="keywords" content="Human Motion Refinement, Human Pose Estimation, Filtering, Denoising, Completion, Kalman Filter">
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<title>FLK: a Filter with Learned Kinematics for Real-time 3D Human Pose Estimation</title>
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<h1 class="title is-1 publication-title">FLK: a Filter with Learned Kinematics for Real-time 3D Human Pose Estimation</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=it&user=m4TgsRMAAAAJ">Enrico Martini</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=it&user=UH1sy14AAAAJ">Michele Boldo</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=it&user=eLA-qo8AAAAJ">Nicola Bombieri</a>,
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block">Department of Engineering for Innovation Medicine,</span><br>
<span class="author-block">University of Verona, Italy</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
There is a growing interest in adopting 3D human pose estimation in safety-critical systems, from healthcare to Industry 5.0.
Nevertheless, when applied in such settings, these neural networks may suffer from estimation inaccuracy.
Besides imprecise or inconsistent annotations in the training dataset, the inaccuracy is caused by poor image quality, rare poses, dropped frames, or heavy occlusions in the scene.
In addition, these scenarios often require the software results to have temporal constraints, such as real-time and zero- or low-latency, which make many of the filtering solutions proposed in the literature inapplicable.
This paper proposes FLK, a Filter with Learned Kinematics, to refine 3D human motion data in real-time and at zero/low latency. The temporal core combines a Kalman filter and a low-pass filter, which learns the motion model through a recurrent neural network.
The spatial core takes advantage of the biomechanical constraints of the human body to provide spatial coherency between keypoints.
The combination of the cores allows the filter to adequately address different types of noise, from jittering to dropped frames.
We test the filter on motion data from multiple datasets and backbones, improving accuracy up to 140 mm with non-Gaussian noise and 53 mm with missing information.
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<!-- <section class="section">
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<h2 class="title is-3">PoseTron: A Multimodal transformer for multi-agent motion prediction</h2>
<p>It features:</p>
<ul>
<li>Multimodal Pose Encoder for encoding pose information for multiple agents</li>
<li>Multimodal Pose Decoder for auto-regressively predicitng future pose information</li>
</ul>
<img src="./static/images/posetron_architecture.png" alt="PoseTron-banner" class="responsive-image">
</section> -->
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<h2 class="title">BibTeX</h2>
<pre><code>@article{Martini2024FLK,
title = {FLK: A filter with learned kinematics for real-time 3D human pose estimation},
ISSN = {0165-1684},
url = {http://dx.doi.org/10.1016/j.sigpro.2024.109598},
DOI = {10.1016/j.sigpro.2024.109598},
journal = {Signal Processing},
publisher = {Elsevier BV},
author = {Martini, Enrico and Boldo, Michele and Bombieri, Nicola},
year = {2024},
month = jul,
pages = {109598}
}
}</code></pre>
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