The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini cuba mengenal pasti sisi jalan yang sedang ditunggang basikal menggunakan kamera biasa untuk merealisasikan sistem navigasi basikal yang canggih dan sistem sokongan keselamatan menunggang basikal. Untuk mengenal pasti kawasan jalan raya, kaedah yang dicadangkan melakukan segmentasi semantik pada imej kamera hadapan yang ditangkap oleh perakam pacuan basikal atau telefon pintar. Jika kawasan jalan raya memanjang dari tengah imej ke kanan, penunggang basikal menunggang di sebelah kiri jalan raya (iaitu, kedudukan menunggang yang betul di Jepun). Sebaliknya, jika kawasan jalan raya memanjang ke kiri, penunggang basikal berada di sebelah kanan jalan raya (iaitu, kedudukan menunggang yang salah di Jepun). Kami menilai ketepatan kaedah yang dicadangkan pada pelbagai lebar jalan dengan volum trafik yang berbeza menggunakan video yang dirakam dengan menunggang basikal di Tsuruoka City, Yamagata Prefecture dan Saitama City, Saitama Prefecture, Jepun. Ketepatan tinggi (>80%) telah dicapai untuk sebarang kombinasi model segmentasi, kaedah pengenalan sisi tunggangan dan keadaan percubaan. Berdasarkan keputusan ini, kami percaya bahawa kami telah merealisasikan kaedah berasaskan pembahagian imej yang berkesan untuk mengenal pasti sisi jalan yang ditunggangi basikal.
Jeyoen KIM
Tsuruoka College
Takumi SOMA
Tsuruoka College,Saitama University
Tetsuya MANABE
Saitama University
Aya KOJIMA
Saitama University
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Salinan
Jeyoen KIM, Takumi SOMA, Tetsuya MANABE, Aya KOJIMA, "Image Segmentation-Based Bicycle Riding Side Identification Method" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 5, pp. 775-783, May 2023, doi: 10.1587/transfun.2022WBP0003.
Abstract: This paper attempts to identify which side of the road a bicycle is currently riding on using a common camera for realizing an advanced bicycle navigation system and bicycle riding safety support system. To identify the roadway area, the proposed method performs semantic segmentation on a front camera image captured by a bicycle drive recorder or smartphone. If the roadway area extends from the center of the image to the right, the bicyclist is riding on the left side of the roadway (i.e., the correct riding position in Japan). In contrast, if the roadway area extends to the left, the bicyclist is on the right side of the roadway (i.e., the incorrect riding position in Japan). We evaluated the accuracy of the proposed method on various road widths with different traffic volumes using video captured by riding bicycles in Tsuruoka City, Yamagata Prefecture, and Saitama City, Saitama Prefecture, Japan. High accuracy (>80%) was achieved for any combination of the segmentation model, riding side identification method, and experimental conditions. Given these results, we believe that we have realized an effective image segmentation-based method to identify which side of the roadway a bicycle riding is on.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022WBP0003/_p
Salinan
@ARTICLE{e106-a_5_775,
author={Jeyoen KIM, Takumi SOMA, Tetsuya MANABE, Aya KOJIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Segmentation-Based Bicycle Riding Side Identification Method},
year={2023},
volume={E106-A},
number={5},
pages={775-783},
abstract={This paper attempts to identify which side of the road a bicycle is currently riding on using a common camera for realizing an advanced bicycle navigation system and bicycle riding safety support system. To identify the roadway area, the proposed method performs semantic segmentation on a front camera image captured by a bicycle drive recorder or smartphone. If the roadway area extends from the center of the image to the right, the bicyclist is riding on the left side of the roadway (i.e., the correct riding position in Japan). In contrast, if the roadway area extends to the left, the bicyclist is on the right side of the roadway (i.e., the incorrect riding position in Japan). We evaluated the accuracy of the proposed method on various road widths with different traffic volumes using video captured by riding bicycles in Tsuruoka City, Yamagata Prefecture, and Saitama City, Saitama Prefecture, Japan. High accuracy (>80%) was achieved for any combination of the segmentation model, riding side identification method, and experimental conditions. Given these results, we believe that we have realized an effective image segmentation-based method to identify which side of the roadway a bicycle riding is on.},
keywords={},
doi={10.1587/transfun.2022WBP0003},
ISSN={1745-1337},
month={May},}
Salinan
TY - JOUR
TI - Image Segmentation-Based Bicycle Riding Side Identification Method
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 775
EP - 783
AU - Jeyoen KIM
AU - Takumi SOMA
AU - Tetsuya MANABE
AU - Aya KOJIMA
PY - 2023
DO - 10.1587/transfun.2022WBP0003
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2023
AB - This paper attempts to identify which side of the road a bicycle is currently riding on using a common camera for realizing an advanced bicycle navigation system and bicycle riding safety support system. To identify the roadway area, the proposed method performs semantic segmentation on a front camera image captured by a bicycle drive recorder or smartphone. If the roadway area extends from the center of the image to the right, the bicyclist is riding on the left side of the roadway (i.e., the correct riding position in Japan). In contrast, if the roadway area extends to the left, the bicyclist is on the right side of the roadway (i.e., the incorrect riding position in Japan). We evaluated the accuracy of the proposed method on various road widths with different traffic volumes using video captured by riding bicycles in Tsuruoka City, Yamagata Prefecture, and Saitama City, Saitama Prefecture, Japan. High accuracy (>80%) was achieved for any combination of the segmentation model, riding side identification method, and experimental conditions. Given these results, we believe that we have realized an effective image segmentation-based method to identify which side of the roadway a bicycle riding is on.
ER -