论文推送 | 利用深度卷积神经网络和固定摄像头量化小微公共空间使用的研究

北京城市实验室BCL · 公众号 · · 2020-10-12 14:41

正文

导读:

本期为大家推介的内容为论文 《Quantifying the usage of small public spaces using deep convolutional neural network 》(使用深度卷积神经网络量化小型公共空间的使用研究).

小型公共空间是为各种活动提供场所的主要建筑环境元素。但是，现有方法难以有效地量化公共场所的使用情况。本文利用深度卷积神经网络通过录制的视频来量化小型公共空间的使用情况，以此作为弥补文献空白的可靠且稳健的方法。首先，我们部署了摄影设备来记录在一定时间内覆盖小型公共空间的最小封闭正方形的视频，然后利用深度卷积神经网络来检测这些视频中的人物，并将其位置从基于图像的位置转换为现实投影坐标的位置。为了验证该方法的准确性和稳健性，我们在北京的一个住宅社区中对我们的方法进行了实验，我们的结果证实可以有效地测量和量化小型公共空间的使用。

— 论文相关 —

题目： 《 Quantifying the usage of small public spaces using deep convolutional neural network 》

作者： Jingxuan Hou,Long Chen, Enjia Zhang, Haifeng Jia , and Ying Long

发表刊物： 《 PLOS ONE 》

URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239390

(点击文末 “阅读原文” 或 复制粘贴至浏览器搜索 可查看)

摘要 ABSTRACT

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.

— 中文 PPT讲解 —