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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65820

    Title: 基於HPSO-TVAC演算法於多目標追蹤系統之研究;Multi-Objects Tracking Based on HPSO-TVAC Algorithm
    Authors: 張榮貴;Chang,Jung-Kuei
    Contributors: 電機工程學系
    Keywords: 粒子群最佳化演算法;自組織隨時調變係數的粒子最佳化演算法;物件追蹤;物件偵測;種子區域生長法;PSO;HPSO-TVAC;object tracking;object detection;seeded region growing method
    Date: 2014-08-20
    Issue Date: 2014-10-15 17:11:03 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 為了提升追蹤目標物的速度和解決目標物被遮蔽的問題,本論文採用改良的粒子最佳化演算法“自組織隨時調變係數的粒子最佳化演算法”來對目標物進行追蹤。
    ;In order to improve the tracking speed and solve the shadowing problem, this paper trace objects by “Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients (HPSO-TVAC)”, which improved from PSO algorithm.
    We use the relationship between the members of the groups to make whole groups go along toward the way of better goals by HPSO-TVAC algorithm, and we also uses “adaptive searching window”. Then the searching window will zoom in or out which depends on global best fitness. While we can find the targets, we will make the searching window contains small, but while we cannot find the targets, we will let the searching window bigger to find the targets.
    The improved seeded region growing method, which we mainly improved the quantity of seeds, is presented in this study. We reduce quantity of seeds to increase the efficiency, and it can let us distinguish between different targets.
    In this study, we use background subtraction to distinguish background and moving objects, and we also use improved seeded region growing method to distinguish different targets. Then we use color histograms to build target models, and trace every targets by HPSO-TVAC algorithm.
    Appears in Collections:[電機工程研究所] 博碩士論文

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