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

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

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8725-8728. https://doi.org/10.24963/ijcai.2024/1018

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.
Keywords:
Computer Vision: CV: Action and behavior recognition
Computer Vision: CV: 3D computer vision
Computer Vision: CV: Applications
Computer Vision: CV: Recognition (object detection, categorization)
Computer Vision: CV: Video analysis and understanding   
Machine Learning: ML: Applications