A real-time wearable AR system for egocentric vision on the edge

Abstract

Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR system, analysing its specifications, overall architecture and core algorithmic components. DARLENE comprises AR glasses and a wearable computing node responsible for several time-critical computation tasks. These include computer vision modules developed for the real-time analysis of dynamic scenes supporting functionalities for instance segmentation, tracking and pose estimation. To meet real-time requirements in limited resources, concrete algorithmic adaptations and design choices are introduced. The proposed system further supports real-time video streaming and interconnection with external IoT nodes. To improve user experience, a novel approach is proposed for the adaptive rendering of AR content by considering the user’s stress level, the context of use and the environmental conditions for adjusting the level of presented information towards enhancing their situational awareness. Through extensive experiments, we evaluate the performance of individual components and end-to-end pipelines. As the proposed system targets time-critical security applications where it can be used to enhance police officers’ situational awareness, further experimental results involving end users are reported with respect to overall user experience, workload and evaluation of situational awareness.