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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/53762

    Title: Moving Object Detection and Tracking Using GMM
    Authors: Lin, Hwei-jen;Yeh, Jih-pin;Wang, Chun-wen;Liang, Feng-ming
    Contributors: 淡江大學資訊工程學系
    Keywords: Detection;tracking;Gaussian mixture model;Particle filters;Sequential K-mean algorithm;Expectation maximization
    Date: 2009-08
    Issue Date: 2011-05-20 09:59:09 (UTC+8)
    Publisher: Allahabad: Pushpa Publishing House
    Abstract: 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.
    Relation: Far East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.69-80
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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