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.

翻译：跨行业领域的法规遵从性审计要求更高的质量保障与可追溯性。当前依赖人工的间歇性审计方法存在显著挑战，可能导致监控过程中出现疏漏。针对这些问题，我们提出了一种采用3D飞行时间相机与RGB相机相结合的实时多模态感知系统，并在边缘AI设备上部署无监督学习技术。该系统能够实现连续目标跟踪，从而提升记录保存效率并减少人工干预。虽然在农业食品设施的刀具消毒场景中验证了系统性能，重点突出了其在RGB相机面临遮挡与低光照问题时的优势，但该技术方案可广泛适用于各类工业监控环境。