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          <h1 class="title is-1 publication-title">COMETH: Convex Optimization for Multiview Estimation and Tracking of Humans</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://scholar.google.com/citations?hl=en&user=m4TgsRMAAAAJ">Enrico Martini</a><sup>a,b</sup>,</span>
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              <a href="https://scholar.google.com/citations?user=GrL4IBwAAAAJ&hl=en&oi=ao">Ho Jin Choi</a><sup>b</sup>,</span>
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              <a href="https://scholar.google.com/citations?hl=en&user=1NQRXHQAAAAJ">Nadia Figueroa</a><sup>b</sup>,</span>
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              <a href="https://scholar.google.com/citations?hl=en&user=eLA-qo8AAAAJ">Nicola Bombieri</a><sup>a</sup>,
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            <span class="author-block"><sup>a</sup>Department of Engineering for Innovation Medicine, University of Verona, Italy.</span><br>
            <span class="author-block"><sup>b</sup>GRASP Laboratory, Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.</span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
In the era of Industry 5.0, <b>monitoring human activity</b> is essential for ensuring both ergonomic safety and overall well-being.
While multi-camera centralized setups improve pose estimation accuracy, they often suffer from high computational costs and bandwidth requirements, limiting scalability and real-time applicability.
Distributing processing across edge devices can reduce network bandwidth and computational load.
On the other hand, the constrained resources of edge devices lead to accuracy degradation, and the distribution of computation leads to temporal and spatial inconsistencies.
We address this challenge by proposing <b>COMETH</b> (Convex Optimization for Multiview Estimation and Tracking of Humans), <b>a lightweight algorithm for real-time multi-view human pose fusion</b> that relies on three concepts:
it integrates <b>kinematic and biomechanical constraints</b> to increase the joint positioning accuracy; it employs <b>convex optimization-based inverse kinematics</b> for spatial fusion; and it implements a <b>state observer</b> to improve temporal consistency.
We evaluate COMETH on both public and industrial datasets, where it outperforms state-of-the-art methods in localization, detection, and tracking accuracy.
The proposed fusion pipeline enables accurate and scalable human motion tracking, making it well-suited for industrial and safety-critical applications.
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        <h2 class="title is-3">Acknowledgments</h2>
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This study was conducted within the MICS (Made in Italy - Circular and Sustainable) Extended Partnership and received funding from Next-Generation EU (Italian PNRR - M4 C2, Invest 1.3 - D.D. 1551.11-10-2022, PE00000004). CUP MICS D43C22003120001 - Cascade funding project CollaborICE. This work has also been supported by the National Science Foundation (NSF) Foundational Research in Robotics (FRR) program under NSF CAREER Award Grant No. FRR-2443721.
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      <h2 class="title is-3">PoseTron: A Multimodal transformer for multi-agent motion prediction</h2>
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    <pre><code>@article{Martini2026,
  title = {COMETH: Convex optimization for multiview estimation and tracking of humans},
  volume = {314},
  ISSN = {0957-4174},
  url = {http://dx.doi.org/10.1016/j.eswa.2026.131728},
  DOI = {10.1016/j.eswa.2026.131728},
  journal = {Expert Systems with Applications},
  publisher = {Elsevier BV},
  author = {Martini,  Enrico and Choi,  Ho Jin and Figueroa,  Nadia and Bombieri,  Nicola},
  year = {2026},
  month = jun,
  pages = {131728}
}
</code></pre>
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