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

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, extended

Summary

CorVS is a novel data-driven method for identity-aware person localization in industrial warehouses, combining fixed camera visual tracking with wearable sensor measurements. Developed to overcome limitations of appearance-based or purely trajectory-based identification in complex real-world settings, CorVS employs a deep learning model to predict correspondence probabilities and reliabilities between visual trajectories and sensor data. This model uses movement speeds, turning rates, linear acceleration, and angular velocity, sampled at 10 Hz with window lengths of 10, 30, or 60 seconds. A subsequent matching algorithm incrementally associates pairs, deferring uncertain decisions. The method was validated using a unique dataset collected from 29 workers in a 29x18 m2 warehouse, utilizing 19 wide-angle cameras streaming at 5 fps and smartphones (ASUS Zenfone 8, Android 13). CorVS demonstrated superior performance over a baseline, with a window size of 600 (60 seconds) achieving nearly 100% Participant Precision.

Key takeaway

For Machine Learning Engineers developing identity-aware localization systems in industrial settings, CorVS offers a robust approach. You should integrate deep learning models that estimate correspondence probabilities and activity-based reliabilities between visual trajectories and sensor data. Consider implementing an incremental matching algorithm that defers uncertain decisions, especially in environments with varied worker activity. This method improves identification accuracy in complex, real-world warehouse operations.

Key insights

CorVS identifies warehouse workers by matching visual trajectories and wearable sensor data using a deep learning model and incremental algorithm.

Principles

• Combine visual and sensor data for robust identification.
• Account for low-activity periods with reliability scores.
• Defer uncertain matches for incremental association.

Method

CorVS uses a DualCNN-Transformer to predict correspondence probabilities and activity-based reliabilities from visual and sensor features. An algorithm then incrementally matches trajectories and sensor data based on these scores and thresholds.

In practice

• Deploy fixed cameras and worker-worn smartphones.
• Collect diverse real-world warehouse activity data.
• Tune window size for precision-recall trade-off.

Topics

• Person Identification
• Warehouse Logistics
• Multi-modal Sensing
• Deep Learning
• Visual Tracking
• Wearable Sensors

Code references

• ultralytics/ultralytics

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.