@article{408b2ec45a914954a803f557e8dcc6cc,
title = "A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision",
abstract = "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 spatiooral 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.",
keywords = "Computer vision, agent-based modelling, objects detection, simulation, urban computing",
author = "Tokuda, {Eric K.} and Yitzchak Lockerman and Ferreira, {Gabriel B.A.} and Ethan Sorrelgreen and David Boyle and Cesar-Jr., {Roberto M.} and Silva, {Claudio T.}",
note = "Funding Information: E. K. Tokuda and Y. Lockerman contributed equally to the article. This work was supported in part by: NSF awards CNS-1229185, CCF-1533564, CNS-1544753, CNS-1730396, CNS-1828576; FAPESP (grants #14/24918-0 and #2015/22308-2); CNPq and CAPES; the Moore-Sloan Data Science Environment at NYU, and C2SMART. C. T. Silva is partially supported by the DARPA D3M program. Authors{\textquoteright} addresses: E. K. Tokuda, G. B. A. Ferreira, and R. M. Cesar-Jr., University of S{\~a}o Paulo, Rua do Matao, 1010, S{\~a}o Paulo, 05508-090, Brazil; emails: {tokudaek, gabriel.augusto.ferreira, rmcesar@}usp.br; Y. Lockerman and C. T. Silva, New York University, 2 MetroTech Center, NY, 11201; emails: {ydl214, csilva}@nyu.edu; E. Sorrelgreen, Carmera, 1100 NE Campus Pkwy Suite 200, Washington, 98105; email: ethan@carmera.co; D. Boyle, Carmera, 20 Jay St Suite 312, New York, 11201; email: david@carmera.co. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. {\textcopyright} 2020 Association for Computing Machinery. 2374-0353/2020/07-ART26 $15.00 https://doi.org/10.1145/3397575 Publisher Copyright: {\textcopyright} 2020 ACM.",
year = "2020",
month = aug,
doi = "10.1145/3397575",
language = "English (US)",
volume = "6",
journal = "ACM Transactions on Spatial Algorithms and Systems",
issn = "2374-0353",
publisher = "Association for Computing Machinery (ACM)",
number = "4",
}