Real-Time Warehouse Monitoring with Ceiling Cameras and Digital Twin for Asset Tracking and Scene Analysis

Jianqiao Cheng (), Connor Verhulst, Pieter De Clercq, Shannon Van De Velde, Steven Sagaert, Marc Mertens, Merwan Birem, Maithili Deshmukh, Neel Broekx, Erwin Rademakers, Abdellatif Bey-Temsamani and Jean-Edouard Blanquart ()
Additional contact information
Jianqiao Cheng: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Connor Verhulst: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Pieter De Clercq: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Shannon Van De Velde: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Steven Sagaert: Productions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Marc Mertens: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Merwan Birem: Productions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Maithili Deshmukh: Codesigns Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Neel Broekx: Productions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Erwin Rademakers: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Abdellatif Bey-Temsamani: Productions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium
Jean-Edouard Blanquart: Motions Corelab, Flanders Make, Gaston Geenslaan 8, 3001 Leuven, Belgium

Logistics, 2025, vol. 9, issue 4, 1-26

Abstract: Background : Effective asset tracking and monitoring are critical for modern warehouse management. Methods : In this paper, we present a real-time warehouse monitoring system that leverages ceiling-mounted cameras, computer vision-based object detection, a knowledge-graph based world model. The system is implemented in two architectural configurations: a distributed setup with edge processing and a centralized setup. Results : Experimental results demonstrate the system’s capability to accurately detect and continuously track common warehouse assets such as pallets, boxes, and forklifts. This work provides a detailed methodology, covering aspects from camera placement and neural network training to world model integration and real-world deployment. Conclusions : Our experiments show that the system achieves high detection accuracy and reliable real-time tracking across multiple viewpoints, and it is easily scalable to large-scale logistics and inventory applications.

Keywords: computer vision; decision support; digital twin; indoor tracking; logistics; property graph; real-time monitoring; smart warehouse; World Model; YOLO (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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