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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2310.03333 |
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| _version_ | 1866912211905019904 |
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| author | Lim, Jia Syuen Wang, Ziwei Liu, Jiajun Khamis, Abdelwahed Arablouei, Reza Barlow, Robert McAllister, Ryan |
| author_facet | Lim, Jia Syuen Wang, Ziwei Liu, Jiajun Khamis, Abdelwahed Arablouei, Reza Barlow, Robert McAllister, Ryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03333 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring Lim, Jia Syuen Wang, Ziwei Liu, Jiajun Khamis, Abdelwahed Arablouei, Reza Barlow, Robert McAllister, Ryan Computer Vision and Pattern Recognition 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. |
| title | Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.03333 |