Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring

Abstract

Regulatory compliance auditing in agrifood processing facilities is crucial for upholding the highest standards of quality assurance and traceability. However, the current manual and intermittent approaches to auditing present significant challenges and risks, potentially leading to gaps or loopholes in the system. To address these shortcomings, we introduce a real-time, multi-modal sensing system that utilizes 3D time-of-flight and RGB cameras and leverages unsupervised learning techniques on edge AI devices. The proposed system enables continuous object tracking, leading to improved efficiency in record-keeping and reduced manual labor. We demonstrate the effectiveness of the system in a knife sanitization monitoring scenario, showcasing its capability to overcome occlusion and low-light performance limitations commonly encountered with conventional RGB cameras.