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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68942

    Title: 基於人工蜂群演算法之物件追蹤研究;Objects Tracking Based on Artificial Bee Colony Algorithm
    Authors: 蔡尚麟;Tsai,Shang-Lin
    Contributors: 電機工程學系
    Keywords: 人工蜂群演算法;物件追蹤;物件偵測;種子區域生長法;ABC;Object tracking;Object detection;Seeded region growing
    Date: 2015-08-18
    Issue Date: 2015-09-23 14:47:06 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來,隨著攝影機與監視器的普及,影像追蹤成為了一個熱門的議題。為了提升追蹤目標物的精準度和解決目標物遮蔽的問題,本論文採用人工蜂群(Artificial Bee Colony; ABC)演算法來對目標物進行即時追蹤。
    ;In recent years, as cameras and monitors become more and more popular, object tracking becomes a hot issue. In order to improve the accuracy of the tracking object and solve the occlusion problem, in this thesis, the Artificial Bee Colony (ABC) algorithm is used for object tracking in real time.
    In terms of object detection, in this thesis, the background subtraction is used for it can cut out complete targets, has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target and calculate its center. Then, for model building, color histograms are used to build target models. In order to avoid the interference of light, in this thesis, the HSV (Hue, Saturation and Value) color space is used. Moreover, for object tracking, in this thesis, the ABC algorithm which has a simple structure is used to find the best solution for it is easily used and its convergence is fast. Occlusion is always a big problem for object tracking. Therefore, in this thesis, the adaptive searching window is applied to exclude occlusion; the searching window will zoom in or out, depending on its fitness value. If the tracking window loses the targets, the searching window will increase. If the tracking window finds the targets, the searching window will adjust to the original size.
    Appears in Collections:[電機工程研究所] 博碩士論文

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