English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 58323/91876 (63%)
造访人次 : 14057974      在线人数 : 74
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115538

    题名: Real-Time Embedded Implementation of Robust Speed-Limit Sign Recognition Using a Novel Centroid-to-Contour Description Method
    作者: Tsai, Chi-Yi;Liao, Hsien-Chen;Hsu, Kuang-Jui
    关键词: Android (operating system);embedded systems;image classification;learning (artificial intelligence);microprocessor chips;smart phones;support vector machines;video streaming
    日期: 2017-09-11
    上传时间: 2018-11-08 12:11:07 (UTC+8)
    出版者: IET
    摘要: 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.
    關聯: IET Computer Vision 11(6), p.407-414
    DOI: 10.1049/iet-cvi.2016.0082
    显示于类别:[電機工程學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈