Young Researcher Paper Award 2021

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語


 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.

MYU Research

(proofreading and recording)

(translation service)

The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 33, Number 1(1) (2021)
Copyright(C) MYU K.K.
pp. 17-34
S&M2435 Research Paper of Special Issue
Published: January 15, 2021

A Method for Detecting Street Parking Using Dashboard Camera Videos [PDF]

Akihiro Matsuda, Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa, and Keiichi Yasumoto

(Received July 29, 2020; Accepted October 21, 2020)

Keywords: street parking, dashboard camera, city sensing, object detection, machine learning

In recent years, street parking in prohibited areas has become a social problem, especially in urban and tourist areas. In addition, because street parking can cause traffic congestion and accidents, real-time detection is required. The detection of street parking has been previously implemented on the basis of comparisons of videos recorded by fixed-point cameras. However, this approach has a limited detection area and low accuracy. To overcome these problems, this study aims towards a real-time street parking detection system that uses dashboard camera videos. We propose a machine learning method based on the characteristics of on-street parked vehicles derived by transforming images into text. The object detection model YOLOv3 was used to analyze videos. We created a dataset based on the coordinate information of 1765 vehicles and the recording vehicle information. We also created a model using random forest and logistic regression algorithms and evaluated it using the holdout and stratified 5-fold validation methods. F-measure values of up to 92% and 89% were obtained for the two types of model, respectively. These results confirm the effectiveness of the proposed street parking detection method based on bounding boxes and recording vehicle data.

Corresponding author: Akihiro Matsuda

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Akihiro Matsuda, Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa, and Keiichi Yasumoto, A Method for Detecting Street Parking Using Dashboard Camera Videos, Sens. Mater., Vol. 33, No. 1, 2021, p. 17-34.

Forthcoming Regular Issues

Forthcoming Special Issues

Special Issue on Biosensors and Biofuel Cells for Smart Community and Smart Life
Guest editor, Seiya Tsujimura (University of Tsukuba), Isao Shitanda (Tokyo University of Science), and Hiroaki Sakamoto (University of Fukui)

Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2021 (IMETI2021)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website

Special Issue on Novel Sensors and Related Technologies on IoT Applications: Part 1-2
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper

Special Issue on Advanced Micro/Nanomaterials for Various Sensor Applications (Selected Papers from ICASI 2021)
Guest editor, Sheng-Joue Young (National United University), Shoou-Jinn Chang (National Cheng Kung University), Liang-Wen Ji (National Formosa University), and Yu-Jen Hsiao (Southern Taiwan University of Science and Technology)
Conference website
Call for paper

Special Issue on Advanced Technologies for Remote Sensing and Geospatial Analysis: Part 3
Guest editor, Dong Ha Lee (Kangwon National University) and Myeong Hun Jeong (Chosun University)
Call for paper

Special Issue on APCOT 2022
Guest editor, Yuelin Wang, Tie Li (Shanghai Institute of Microsystem and Information Technology) and Qingan Huang (Southeast University)
Conference website
Call for paper

Copyright(C) MYU K.K. All Rights Reserved.