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, Jean-Edouard Blanquart · Logistics · 2025
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.