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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95854

    Title: Moving Object Detection and Tracking
    Authors: Lin, Hwei-Jen;Liang, Feng-Ming;Wang, Chun-Wei;Yang, Fu-Wen
    Contributors: 淡江大學資訊工程學系
    Keywords: Detection;Tracking;Gaussian mixture model (GMM);Particle filter (PF);Sequential K means algorithm;Expectation maximization (EM)
    Date: 2008-11
    Issue Date: 2014-02-13
    Publisher: 臺北縣淡水鎮 : 淡江大學
    Abstract: For object detection and tracking, we use amodified version of Gaussian Mixture Models(GMMs) to construct background, which is thensubtracted from the image to obtain the foregroundwhere the moving objects locate. We then performsome operations, including shadow removal, edgedetection, and connected component analysis, tolocalize each moving object in the foreground. As soon as an object is detected it is then trackedin the following frames by the use of Particle Filters(PF). PF is effective but the dimension of its statespace is high so as the tracked objects tend to beshifting. To reduce this problem we modify theparticle filtering by carrying out tracking over theforeground portion instead of the whole image. Withthe use of the modified versions of GMMs and PFs,our system was proved to have high accuracy rate ofdetection/tracking and satisfactory time efficiency.
    Relation: 第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.809-813
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

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