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

• URL: http://arxiv.org/abs/2310.03333v1
• Date: Thu, 5 Oct 2023 06:31:38 GMT
• Title: Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring
• Authors: Jia Syuen Lim, Ziwei Wang, Jiajun Liu, Abdelwahed Khamis, Reza Arablouei, Robert Barlow, Ryan McAllister
• Abstract summary: We introduce a real-time, multi-modal sensing system employing 3D time-of-flight and RGB cameras. This enables continuous object tracking thereby enhancing efficiency in record-keeping and minimizing manual interventions.
• Score: 8.990839181608505
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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