Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring
This addresses regulatory compliance monitoring for industrial domains like agrifood, but it appears incremental as it applies existing multi-modal and edge AI techniques to a specific application.
The paper tackles the problem of manual and intermittent regulatory compliance auditing by introducing a real-time multi-modal sensing system using 3D time-of-flight and RGB cameras with unsupervised learning on edge AI devices, resulting in continuous object tracking that enhances efficiency and minimizes manual interventions, validated in a knife sanitization context in agrifood facilities.
Regulatory compliance auditing across diverse industrial domains requires heightened quality assurance and traceability. Present manual and intermittent approaches to such auditing yield significant challenges, potentially leading to oversights in the monitoring process. To address these issues, we introduce a real-time, multi-modal sensing system employing 3D time-of-flight and RGB cameras, coupled with unsupervised learning techniques on edge AI devices. This enables continuous object tracking thereby enhancing efficiency in record-keeping and minimizing manual interventions. While we validate the system in a knife sanitization context within agrifood facilities, emphasizing its prowess against occlusion and low-light issues with RGB cameras, its potential spans various industrial monitoring settings.