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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72148

    Title: 深度與色彩資訊於PMHPSO-TVAC之即時多目標追蹤應用;Object Tracking Based On PMHPSO-TVAC with Color and Depth Data in Real Time
    Authors: 李政勳;Li,Zheng-Xun
    Contributors: 電機工程學系
    Keywords: PMHPSO-TVAC演算法;目標物追蹤;目標物偵測;種子區域生長法;深度資料;PMHPSO-TVAC;Object tracking;Object detection;Depth data;Seed Region Growing Method
    Date: 2016-07-15
    Issue Date: 2016-10-13 14:28:32 (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,物件偵測及物件追蹤是電腦視覺領域中一門重要且充滿挑戰性的研究課題。針對單一物件追蹤而言,其困難點在於複雜環境與追蹤物件的高變異性,複雜環境因素包含:光線、角度,而追蹤物外觀的變異性又可分為剛體(角度變化)與非剛體形變,以及遮蔽等問題。
    ;In recent years, with the popularity of the camera and monitor, object detection and object tracking field are important and challenging research topic. For object tracking, it is difficult to track objects in complex environments. In order to improve the tracking speed and solve the shadowing problem, this paper uses Position Mutated Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PMHPSO-TVAC) algorithm for object tracking in real time. In terms of object detection, in this study, the background subtraction is used. And can cut out complete targets. The background subtraction has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target. Then, for model building, color histograms are used to build target models. However, in no-light environment, we can’t track any target, so this paper construct depth histogram object model to compensate for object model. Finally, we used the depth histogram and color histogram model with PMHPSO-TVAC algorithms for multi-target tracking.
    Appears in Collections:[電機工程研究所] 博碩士論文

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