A Cloud-Edge Collaborative Framework for Personnel Identification via Wearable Smart Glasses

Abstract

This paper presents a cloud-edge collaborative framework for intelligent personnel identification and crowd sensing based on wearable AI smart glasses in complex environments. To overcome the computational and power constraints of edge devices, the system adopts a cloud-edge architecture where the smart glasses perform lightweight face detection, adaptive ROI cropping, and quality-aware compression, while the cloud server executes high-dimensional feature extraction and identity matching. For identity verification, a temporal multi-frame weighted smoothing strategy integrates cosine similarity scores across consecutive frames, achieving an overall accuracy of 96.1% in experiments. For database-free scenarios, an identity-agnostic crowd sensing pipeline combines human keypoint-based frontal orientation filtering, appearance-based semantic role discrimination for staff exclusion, and a cross-frame tracking mechanism fusing person re-identification features with spatial kinematic association, attaining a pedestrian recognition accuracy of 97.8% and achieving complete staff filtering in the tested controlled scenarios. Experimental results on real-world hardware demonstrate that the proposed framework effectively balances real-time responsiveness, analytical accuracy, and privacy preservation, providing a scalable solution for intelligent personnel management in complex dynamic environments.