DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING

Kazuki Murayama, Kenji Kanai, Masaru Takeuchi, Heming Sun, Jiro Katto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, requirement of city monitoring and maintenance using ICT techniques increases with the help of transportation system. In addition, the spread of COVID-19 has increased the demand for managing pedestrian traffic volume. To contribute to these trends, in this paper, we propose a new pedestrian radar map system in order to estimate pedestrian density on streets and sidewalks. Our system uses e-bikes to collect 360-degree images and visualize pedestrian positions as a radar map. In evaluations, we confirm the accuracies of the radar maps and pedestrian density by using KITTI dataset and by carrying out a field experiment.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages230-234
Number of pages5
ISBN (Electronic)9781665441155
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 2021 Sep 192021 Sep 22

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period21/9/1921/9/22

Keywords

  • Deep learning
  • Density estimation
  • Distance estimation
  • Mobile sensing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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