Work

EasyVizAR: Supporting First Responders Through the Use of Collaborative Augmented Reality Tools

Year: 2025

Type: article

Abstract: First responders operate in high-stakes environments, demanding rapid, accurate decision-making. Recent research has provided recommendations on how Augmented Reality (AR) could be utilized to support their efforts. Building off of these studies, this paper presents EasyVizAR, an AR system designed to enhance situational awareness and operational efficiency in challenging indoor scenarios. Leveraging edge computing and advanced computer vision techniques, EasyVizAR addresses critical challenges, such as object detection, localization, and information sharing. This research details the system’s architecture, including its use of ParaDrop-based edge computing, and explores its application in rescue and active-shooter scenarios. We present our work in developing key features, including real-time object saliency cues, improved object detection, person identification, multi-user 3D map generation and visualization, and multimodal AR navigation cues.

Source: Applied Sciences

DOI: 10.3390/app152111498

Authors: Kevin Ponto, Lance Hartung, Yuhang Zhao, Bryce Sprecher, Ross Tredinnick, Suman Banerjee

Institutions: University of Wisconsin–Madison, Wisconsin Institutes for Discovery

Language: English

Topic: Augmented Reality Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Institute of Standards and Technology