English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 53694/88316 (61%)
造访人次 : 10279586      在线人数 : 23
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/987654321/53762

    题名: Moving Object Detection and Tracking Using GMM
    作者: Lin, Hwei-jen;Yeh, Jih-pin;Wang, Chun-wen;Liang, Feng-ming
    贡献者: 淡江大學資訊工程學系
    关键词: Detection;tracking;Gaussian mixture model;Particle filters;Sequential K-mean algorithm;Expectation maximization
    日期: 2009-08
    上传时间: 2011-05-20 09:59:09 (UTC+8)
    出版者: Allahabad: Pushpa Publishing House
    摘要: For object detection and tracking, we use a modified version of Gaussian Mixture Models (GMMs) to construct the background, and then subtract it from the image to obtain the foreground where the moving objects are located. We then perform some operations, including shadow removal, edge detection, and connected component analysis to localize each moving object in the foreground. As soon as an object is detected, it is tracked in the subsequent frames using a Particle Filter (PF). The PF is effective, but the dimension of its state space is high since the tracked objects tend shift. To reduce this problem, we modify the particle filter by tracking over the foreground portion instead of the entire image. Using modified versions of both the GMM and PF, our system proves to have a high accuracy rate for detection/tracking and satisfactory time efficiency.
    關聯: Far East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.69-80
    显示于类别:[資訊工程學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数
    0974-3261_3(2)p69-80.pdf7644KbAdobe PDF2检视/开启



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