Device-Edge-Cloud Collaborative Acceleration Method Towards Occluded Face Recognition in High-Traffic Areas

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE

A Framework to Support Social Distancing Management based on 5G and Accelerated Edge Cloud

In 5G Release 17 specific work items are dealing with medical applications. Moreover, the COVID-19 pandemic has accelerated the adoption of mobile-health (m-health) and e-health. This paper proposes the implementation of a m-health framework supporting social distancing management. Experimental results show that by exploiting 5G connectivity and the computational power provided by an accelerated edge cloud, the proposed framework can perform social distancing verification faster than a user equipment (UE)-based deployment. © 2021 IEEE.