A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision
A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision
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DOI：
10.1145/3397575
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发表时间：
2018-11
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影响因子：
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通讯作者：
Eric K. Tokuda;Y. Lockerman;Gabriel B. A. Ferreira;E. Sorrelgreen;David E. Boyle;R. Cesar;Cláudio T. Silva
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文献类型：
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作者：
Eric K. Tokuda;Y. Lockerman;Gabriel B. A. Ferreira;E. Sorrelgreen;David E. Boyle;R. Cesar;Cláudio T. Silva
对于众多城市应用程序，包括运输网络的设计和业务发展计划，对人的动态的理解是必不可少的。行人计数通常需要使用手动或技术手段来计算每个感兴趣的位置的个人。但是，此类方法并未扩展到城市的规模，并且在这里提出了一种新的填补此空白的方法。在这个项目中，我们使用了纽约市图像的大量密集数据集以及计算机视觉技术来构建相对人物密度的时空图。由于最先进的计算机视觉方法的局限性，这种自动检测人本质上会遇到错误。我们将这些错误建模为概率过程，为此我们提供了理论分析和彻底的数值模拟。我们证明，在我们的假设中，我们的方法可以提供对人密度的合理估计，并为所产生的错误提供理论界限。
An understanding of person dynamics is indispensable for numerous urban applications, including the design of transportation networks and planning for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods do not scale to the size of a city and a new approach to fill this gap is here proposed. In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density. Due to the limitations of state-of-the-art computer vision methods, such automatic detection of person is inherently subject to errors. We model these errors as a probabilistic process, for which we provide theoretical analysis and thorough numerical simulations. We demonstrate that, within our assumptions, our methodology can supply a reasonable estimate of person densities and provide theoretical bounds for the resulting error.