DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING

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

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
出版社IEEE Computer Society
ページ230-234
ページ数5
ISBN(電子版)9781665441155
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
継続期間: 2021 9月 192021 9月 22

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2021-September
ISSN(印刷版)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
国/地域United States
CityAnchorage
Period21/9/1921/9/22

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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