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

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.

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Publication Details

Journal
Applied Sciences
Published
2025-10-28
DOI
https://doi.org/10.3390/app152111498
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
0.00

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

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

Suman Banerjee, Ross Tredinnick, Kevin Ponto, Lance Hartung, Bryce Sprecher, Yuhang Zhao
article en

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.

Applied SciencesVol. 15(21)
University of Wisconsin–Madison (US), Wisconsin Institutes for Discovery (US)
National Institute of Standards and Technology
Openalex Percentile: Top 69%
Augmented Reality Applications
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