@article{ref1,
title="A study of object detection methodologies applicable to walking assistant devices to identify crosswalks and signals",
journal="Transactions of Society of Automotive Engineers of Japan",
year="2022",
author="Kawamura, Hiroaki and Shintani, Kohei and Mima, Hiroki and Taniguchi, Mashio",
volume="53",
number="3",
pages="611-616",
abstract="This paper describes a visual object recognition method applicable for smart devices to identify crosswalks and pedestrian signals. The device, which is incorporated into a grip of handheld equipment, is capable to support users which have difficulty to recognize crosswalks. In this paper, a novel object detection methodology is proposed, which is a combination of CNN and computational visualization technology. By using the proposed object detection methodology, a smart assistant system for crossing roads is developed. The developed method exhibited highly accuracy recognition of the target objects.   ===  本研究では，グリップ部にカメラと振動モータを設置した小型歩行支援デバイスを対象として，撮影画像から横断歩道および歩行者信号を認識し，デバイス使用者に通知する技術を開発した．カメラ撮影画像による横断歩道および歩行者信号の検出時において，より高精度に検出する技術を開発し，実験によりその精度を検証した．<p /> <p>Language: ja</p>",
language="ja",
issn="0287-8321",
doi="10.11351/jsaeronbun.53.611",
url="http://dx.doi.org/10.11351/jsaeronbun.53.611"
}