Deep Learning-Based Occupancy Detection and Visualization for Architecture and Urban Data - Towards Augmented Reality and GIS Integration for Improved Safety and Emergency Response Modeling -

Dongyoun Shin

doi:10.13161/kibim.2023.13.2.029

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2023 Vol.13, Issue 2 Preview Page Next Page

Research Article

Deep Learning-Based Occupancy Detection and Visualization for Architecture and Urban Data - Towards Augmented Reality and GIS Integration for Improved Safety and Emergency Response Modeling - 건물 내 재실자 감지 및 시각화를 위한 딥러닝 모델 - 증강현실 및 GIS 통합을 통한 안전 및 비상 대응 개선모델 프로토타이핑 -

30 June 2023. pp. 29-36

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Abstract

This study explores the potential of utilizing video-based data analysis and machine learning techniques to estimate the number of occupants within a building. The research methodology involves developing a sophisticated counting system capable of detecting and tracking individuals' entry and exit patterns. The proposed method demonstrates promising results in various scenarios; however, it also identifies the need for improvements in camera performance and external environmental conditions, such as lighting. The study emphasizes the significance of incorporating machine learning in architectural and urban planning applications, offering valuable insights for the field. In conclusion, the research calls for further investigation to address the limitations and enhance the system's accuracy, ultimately contributing to the development of a more robust and reliable solution for building occupancy estimation.

Keywords

Machine Learning

Urban Data Analysis

Occupancy Detection

Mobility Patterns

Emergency Response

키워드

머신러닝

도시분석

재실자감지

이동행태

재난재해

References

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Information

Publisher :Journal of KIBIM
Publisher(Ko) :한국BIM학회
Journal Title :Journal of KIBIM
Journal Title(Ko) :한국BIM학회논문집
Volume : 13
No :2
Pages :29-36
Received Date : 2023-03-06; 2023-03-26
Accepted Date : 2023-04-06
DOI :https://doi.org/10.13161/kibim.2023.13.2.029

Journal of KIBIM ISSN:2288-1697(Print) 2288-1697(Online) 한국BIM학회논문집

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