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

TL;DR

This paper presents a real-time, multi-modal sensing system using 3D and RGB cameras with unsupervised edge AI for continuous object tracking, improving industrial compliance monitoring efficiency and accuracy.

Contribution

It introduces a novel multi-modal sensing and unsupervised learning system for real-time object detection and tracking on edge devices in industrial environments.

Findings

Effective in occlusion and low-light conditions

Validated in a knife sanitization context

Enhances compliance monitoring efficiency

Abstract

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.