A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection-Reference-Cited by-同舟云学术

A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection

Published:2022-10-01 Issue:10 Volume:E105.D Page:1712-1720
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

HUANG Wenhao¹,TSUGE Akira¹,CHEN Yin²,OKOSHI Tadashi³,NAKAZAWA Jin³

Affiliation:

1. Graduate school of Media and Governance, Keio University

2. Reitaku University

3. Faculty of Environment and Information Studies, Keio University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/10/E105.D_2022PCP0007/_pdf

Reference24 articles.

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2. [2] L. Kanagawa Chuo Kotsu Co., “Coronavirus Infection Prevention Strategies (Online source).” https://www.kanachu.co.jp/dia/news/detail?tbl=4&tid=70.

3. [3] Y. News, “Infected person gets on the bus, female driver positively `I think I took all possible measures (Online source).” https://www.yomiuri.co.jp/national/20200818-OYT1T50074.

4. [4] S. Bus and NAVITIME, “Support for Displaying Real-time Bus Crwodedness Levels.” https://www.seibubus.co.jp/news/uploads/bada778f26e2075dbc54dbb5cc116f05eb43cac7.pdf.

5. [5] Y. Furuya, H. Asahina, M. Yoshida, and I. Sasase, “Indoor crowd estimation scheme using the number of wi-fi probe requests under mac address randomization,” IEICE Trans. Inf. & Syst., vol.E104.D, no.9, pp.1420-1426, 2021. 10.1587/transinf.2020edp7228

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