DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING

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

doi:10.1109/ICIP42928.2021.9506522

DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING

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

研究成果: Conference contribution

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	https://doi.org/10.1109/ICIP42928.2021.9506522
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 19 → 2021 9月 22

出版物シリーズ

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

Conference

Conference	2021 IEEE International Conference on Image Processing, ICIP 2021
国/地域	United States
City	Anchorage
Period	21/9/19 → 21/9/22

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

UN SDG

この成果は、次の持続可能な開発目標に貢献しています

文献へのアクセス

10.1109/ICIP42928.2021.9506522

他のファイルとリンク

引用スタイル

Murayama, K., Kanai, K., Takeuchi, M., Sun, H., & Katto, J. (2021). DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING. In 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings (pp. 230-234). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2021-September). IEEE Computer Society. https://doi.org/10.1109/ICIP42928.2021.9506522

DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING. / Murayama, Kazuki; Kanai, Kenji; Takeuchi, Masaru その他.

2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings. IEEE Computer Society, 2021. p. 230-234 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2021-September).

研究成果: Conference contribution

Murayama, K, Kanai, K, Takeuchi, M, Sun, H & Katto, J 2021, DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING. in 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2021-September, IEEE Computer Society, pp. 230-234, 2021 IEEE International Conference on Image Processing, ICIP 2021, Anchorage, United States, 21/9/19. https://doi.org/10.1109/ICIP42928.2021.9506522

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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.",

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author = "Kazuki Murayama and Kenji Kanai and Masaru Takeuchi and Heming Sun and Jiro Katto",

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AU - Katto, Jiro

N1 - Funding Information: This paper is supported by the verification-style research & development program for solving regional challenges using datacooperationandutilization by NICT, Japan. Publisher Copyright: © 2021 IEEE

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AB - 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.

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KW - Distance estimation

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DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING

抄録

出版物シリーズ

Conference

ASJC Scopus subject areas

UN SDG

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