CorVS+: Correspondence-Driven Association of Video Trajectories and Sensors for Identity-Aware Person Localization in Warehouses

Abstract:Logistics warehouses have struggled with labor shortages, but the inbound processes remain particularly human-powered. Worker location data is a key to higher productivity in such cases. Fixed cameras are a promising tool for localization, as they also offer valuable environmental information such as package status. However, identifying individuals from visual data alone is often impractical. To enable identity-aware localization, prior studies have attempted to identify people in videos by associating their trajectories with wearable sensor measurements. Although this appearance-independent approach has several advantages, existing methods may fail under real-world conditions. Therefore, we propose CorVS+, a novel data-driven person identification framework based on the correspondence between visual tracking trajectories and sensor measurements. Firstly, our deep learning model predicts the correspondence probabilities and reliabilities for every pair of a trajectory and sensor measurements. Secondly, our algorithm matches the pairs over time based on the model predictions. We developed a dataset comprising 27 hours of sensor measurements and 38 km of trajectories in a warehouse. This dataset covers actual activities and challenging situations, such as multiple stationary workers inspecting items. The evaluation indicated the superiority of CorVS+ over existing methods and the effectiveness of its unique designs for industrial-scale settings. The model and dataset will be available at this https URL.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2510.26369 [cs.LG]

(or arXiv:2510.26369v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2510.26369 Focus to learn more arXiv-issued DOI via DataCite

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