A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision

Eric K. Tokuda, Yitzchak Lockerman, Gabriel B.A. Ferreira, Ethan Sorrelgreen, David Boyle, Roberto M. Cesar-Jr., Claudio T. Silva

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number3397575
JournalACM Transactions on Spatial Algorithms and Systems
Issue number4
StatePublished - Aug 2020


  • Computer vision
  • agent-based modelling
  • objects detection
  • simulation
  • urban computing

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Computer Science Applications
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics


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