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.

通过实时的多模式传感系统和边缘智能设备上的无监督学习技术，我们介绍了一种针对不同工业领域的监管合规审计的方法，提高了记录保留的效率并减少了人工干预。