改良凝聚式階層演算法及改良色彩空間影像技術於無線監控自走車之路徑追蹤

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姓名

楊政樺(Zheng-Hua Yang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

改良凝聚式階層演算法及改良色彩空間影像技術於無線監控自走車之路徑追蹤
(Wireless Monitoring Self-Propelled Vehicle Based on Improved Agglomerative Hierarchical Algorithm and Color Space in Image Tracking)

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摘要(中)

本文提出一種改進的即時追蹤演算法藉由改良凝聚式階層演算法和色彩空間，同時也結合了背景相減法、改良種子生長法和無線監控端，來實現即時追蹤自走車。在追蹤前對每個目標物進行顏色直方圖的建立與儲存，以便提升在追蹤時的速度。
由於計算機技術和半導體技術的進步，近年來處理器的速度大大提高。越來越複雜的算法可以用於偵測或追蹤，並且目前相關的研究帶來了高效率的偵測和追蹤。科技的進步使的演算法在速度上取得了重大突破，可以實現偵測和追蹤系統。
基本上，過去幾年提出的物體跟追蹤法一般可分為以下幾類：基於區域式的追蹤，主動式輪廓追蹤，基於特徵的追蹤和基於模型的追蹤。雖然這些追蹤方法已經開發了很長時間，但仍然有許多問題需要解決。例如，複雜的背景和陰影可能會影響追蹤。本文使用改良的凝聚式階層演算法和改良顏色空間進行追蹤，可以有效地解決陰影問題。

摘要(英)

This paper proposes an improved tracking algorithm by using “agglomerative hierarchical algorithm ” and “ color space ” for real-time tracking of the targets, which combines three methods to realize an automatic tracking system in real-time on wireless monitoring self-propelled vehicles. There are three methods including the background subtraction method, the seeded region growing method, and the wireless monitoring system to construct the color histogram models for each targets. Finally, the obtained image tracking pictures can be saved into flash memory to speed up loading.
Due to the advance in computer technologies and the semiconductor technologies, the speed of the processors have been dramatically improved in recent years. More and more complex algorithms then can be used to detect or track objects, and the relating studies flourishes presently bringing about high efficiencies of identifying and tracking objects. It truly makes a major breakthrough in algorithms, and the real-time object tracking system can be achieved.
Basically, the object tracking methods proposed in the past years can be generally divided into these categories: region-based tracking, active contour-based tracking, feature-based tracking and model-based tracking. Although visual tracking has been developed for a long time, it still has many problems which needed to be solved. For example, the complex background and the shadow may have an influence on tracking object. This paper uses improved agglomerative hierarchical algorithm and color space for object tracking that can effectively solve the shadowing problems.

關鍵字(中)

★ 物體追蹤
★ 種子區域生長演算法
★ 改良凝聚式階層演算法
★ 改良型YUV
★ 行程標記法

關鍵字(英)

論文目次

目錄
中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 xi
第一章緒論 1
1-1 簡介 1
1-2 研究動機 1
1-3 研究方法 2
1-4 文獻回顧與探討 2
1-5 主要成果與貢獻 5
1-6 論文架構 6
第二章系統架構與系統描述 7
2-1 外部硬體 7
2-2 內部軟體 13
2-3 系統架構 17
第三章偵測目標物與建模 19
3-1 RGB轉改良型YUV 20
3-2 目標物體偵測 21
3-2-1 連續影像相減法（Temporal difference） 21
3-2-2 光流法（Optical flow） 21
3-2-3 時間軸上的中間值法(Temporal median) 22
3-2-4 非前景像素更新法(Selective update using non-foreground pixels) 22
3-2-5 卡爾曼波器(Kalman filter) 22
3-2-6 高斯混合(Mixture of Gaussians) (MoG) 22
3-2-7 背景相減法(Background substraction) 23
3-3 形態學處理 24
3-3-1 侵蝕(Erosion) 24
3-3-2 膨脹(Dilation) 26
3-3-3 斷開(Opening) 28
3-3-4 閉合(Closing) 29
3-4 影像分割 30
3-4-1 區域分裂與合併(Region Splitting and Merging) 31
3-4-2 行程標記法 32
3-4-3 種子區域生長法(Seeded Region Growing) 34
3-4-4 改良種子區域生長法 37
3-5 目標物建模 42
第四章演算法追蹤目標物 44
4-1 集群分析演算法基本概念 45
4-2 相似度的計算與測量 46
4-2-1 單一連鎖法(single linkage) 48
4-2-2 完全連鎖法(complete linkage) 49
4-2-3 中心法(centroid method) 50
4-2-4 華德法(Ward’s methods) 50
4-3 層次集群方法與非層次集群方法 51
4-3-1 層次集群方法(hierarchical methods) 52
4-3-2 非層次集群方法(non-hierarchical methods) 56
4-4 利用PID控制鏡頭上與車子上的馬達 58
4-5 探討了遮蔽問題與使用的改善方法 64
4-6 系統流程圖 67
第五章實驗結果與討論 69
5-1 模擬實驗 69
5-2 模擬影像追蹤 74
5-3 改良型YUV與一般的YUV 75
5-4 實際實驗 77
第六章結論與建議 88
6-1 結論 88
6-2 建議 88
參考文獻 90
附錄 94

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審核日期

2017-7-19

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