Real-time Multi-modal Object Detection and Tracking on Edge for
  Regulatory Compliance Monitoring
Jia Syuen Lim Ziwei Wang Jiajun Liu Abdelwahed Khamis Reza Arablouei Robert Barlow Ryan McAllister
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.