Beacon-based Indoor Fire Evacuation System using Augmented Reality and Machine Learning

Hwa-Cho Lee, Dohyun Chung, S. Kim, Jiwon Lim, Yoonha Bahng, Suhyun Park, Anthony H. Smith
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Abstract

The inefficiency of fire evacuation has been the issue since the present evacuation method is unsuitable for complex buildings. In order to improve the evacuation system, this paper aims at three main components. First, Kalman Filter and deep learning models were utilized to estimate the user’s location accurately. Second, Q-learning based evacuation algorithm was designed to deal with various fire situations. Lastly, AR and a 2D map offer effective navigation systems. The proposed system offers the safest path based on accurate location with a user-friendly visual supplement.
使用增强现实技术和机器学习的基于信标的室内火灾疏散系统
由于现有的疏散方法不适合复杂的建筑物,因此存在疏散效率低的问题。为了完善疏散系统,本文主要从三个方面进行研究。首先,利用卡尔曼滤波和深度学习模型对用户位置进行准确估计;其次,设计了基于q学习的疏散算法,以应对各种火灾情况。最后,AR和2D地图提供了有效的导航系统。该系统提供了基于准确位置的最安全路径,并提供了用户友好的视觉补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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