Real-Time Embedded Implementation of Robust Speed-Limit Sign Recognition Using a Novel Centroid-to-Contour Description Method

doi:10.1049/iet-cvi.2016.0082

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Journal Article > Item 987654321/115538

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115538

Title:	Real-Time Embedded Implementation of Robust Speed-Limit Sign Recognition Using a Novel Centroid-to-Contour Description Method
Authors:	Tsai, Chi-Yi;Liao, Hsien-Chen;Hsu, Kuang-Jui
Keywords:	Android (operating system);embedded systems;image classification;learning (artificial intelligence);microprocessor chips;smart phones;support vector machines;video streaming
Date:	2017-09-11
Issue Date:	2018-11-08 12:11:07 (UTC+8)
Publisher:	IET
Abstract:	Traffic sign recognition is a very important function in automatic driving assistance systems (ADAS). This study addresses the design and implementation of a vision-based ADAS based on an image-based speed-limit sign (SLS) recognition algorithm, which can automatically detect and recognise SLS on the road in real-time. To improve the recognition rate of SLS having different orientations and scales in the image, this study also presents a new sign content description algorithm, which describes the detected road sign using centroid-to-contour (CtC) distances of the extracted sign content. The proposed CtC descriptor is robust to translation, rotation and scale changes of the SLS in the image. This advantage improves the recognition accuracy of a support vector machine classifier trained using a large database of traffic signs. The proposed SLS recognition method had been implemented on two different embedded platforms, each of them equipped with an ARM-based Quad-Core CPU running Android 4.4 operating system. Experimental results validate that the proposed method not only provides a high recognition rate, but also achieves real-time performance up to 30 frames per second for processing 1280 × 720 video streams running on a commercial ARM-based smartphone.
Relation:	IET Computer Vision 11(6), p.407-414
DOI:	10.1049/iet-cvi.2016.0082
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	146	View/Open

Loading...