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

arXiv: https://arxiv.org/abs/2310.03333 · HTML: https://mlsanity.com/paper/2310.03333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization](https://mlsanity.com/paper/2108.08166/) (2021)

## Abstract (arXiv)

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.