COMETH: Convex Optimization for Multiview Estimation and Tracking of Humans
This work addresses the problem of high computational costs and accuracy degradation in distributed multi-camera human monitoring for industrial and safety-critical applications, representing an incremental improvement with specific gains.
The paper tackles the challenge of accurate and scalable real-time human pose estimation in multi-camera setups by proposing COMETH, a lightweight algorithm that integrates kinematic and biomechanical constraints, convex optimization, and a state observer, which outperforms state-of-the-art methods in localization, detection, and tracking accuracy on public and industrial datasets.
In the era of Industry 5.0, monitoring human activity 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 COMETH (Convex Optimization for Multiview Estimation and Tracking of Humans), a lightweight algorithm for real-time multi-view human pose fusion that relies on three concepts: it integrates kinematic and biomechanical constraints to increase the joint positioning accuracy; it employs convex optimization-based inverse kinematics for spatial fusion; and it implements a state observer 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. The code is publicly available at https://github.com/PARCO-LAB/COMETH.