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Quantifying the usage of small public spaces using deep convolutional neural network
Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this paper, we utilized a deep convolutional neural network...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531796/ https://www.ncbi.nlm.nih.gov/pubmed/33006974 http://dx.doi.org/10.1371/journal.pone.0239390 |
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author | Hou, Jingxuan Chen, Long Zhang, Enjia Jia, Haifeng Long, Ying |
author_facet | Hou, Jingxuan Chen, Long Zhang, Enjia Jia, Haifeng Long, Ying |
author_sort | Hou, Jingxuan |
collection | PubMed |
description | Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this paper, we utilized a deep convolutional neural network to quantify the usage of small public spaces through recorded videos as a reliable and robust method to bridge the literature gap. To start with, we deployed photographic devices to record videos that cover the minimum enclosing square of a small public space for a certain period of time, then utilized a deep convolutional neural network to detect people in these videos and converted their location from image-based position to real-world projected coordinates. To validate the accuracy and robustness of the method, we experimented our approach in a residential community in Beijing, and our results confirmed that the usage of small public spaces could be measured and quantified effectively and efficiently. |
format | Online Article Text |
id | pubmed-7531796 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75317962020-10-08 Quantifying the usage of small public spaces using deep convolutional neural network Hou, Jingxuan Chen, Long Zhang, Enjia Jia, Haifeng Long, Ying PLoS One Research Article Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this paper, we utilized a deep convolutional neural network to quantify the usage of small public spaces through recorded videos as a reliable and robust method to bridge the literature gap. To start with, we deployed photographic devices to record videos that cover the minimum enclosing square of a small public space for a certain period of time, then utilized a deep convolutional neural network to detect people in these videos and converted their location from image-based position to real-world projected coordinates. To validate the accuracy and robustness of the method, we experimented our approach in a residential community in Beijing, and our results confirmed that the usage of small public spaces could be measured and quantified effectively and efficiently. Public Library of Science 2020-10-02 /pmc/articles/PMC7531796/ /pubmed/33006974 http://dx.doi.org/10.1371/journal.pone.0239390 Text en © 2020 Hou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hou, Jingxuan Chen, Long Zhang, Enjia Jia, Haifeng Long, Ying Quantifying the usage of small public spaces using deep convolutional neural network |
title | Quantifying the usage of small public spaces using deep convolutional neural network |
title_full | Quantifying the usage of small public spaces using deep convolutional neural network |
title_fullStr | Quantifying the usage of small public spaces using deep convolutional neural network |
title_full_unstemmed | Quantifying the usage of small public spaces using deep convolutional neural network |
title_short | Quantifying the usage of small public spaces using deep convolutional neural network |
title_sort | quantifying the usage of small public spaces using deep convolutional neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531796/ https://www.ncbi.nlm.nih.gov/pubmed/33006974 http://dx.doi.org/10.1371/journal.pone.0239390 |
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