本論文主要探討水下動態目標物追蹤與估測的問題，適用於水下複雜環境之即時偵測，能夠有效降低光源、複雜背景與遮蔽物的干擾。本實驗主要是透過影像視覺偵測目標物之後，結合卡爾曼濾波器估測出目標物下一瞬間的位置，達到提升運算速率，有效降低在畫面中相似目標物的背景干擾，使PTZ攝影機能夠自動的追蹤目標物，並框選於畫面中央位置。
在實驗方面，本論文整合影像視覺回授、卡爾曼濾波器估測與PTZ攝影機控制，期望達到水下動態目標物追蹤與估測能力。經實驗結果顯示，卡爾曼濾波器所估測出的估測位置大致都能超前於量測所得到的量測位置；對於強烈光源的影響，則利用色彩空間YCrCb有效的將光源分離。在降低隨機雜訊的部分，結合中值濾波器與型態學中的侵蝕與膨脹，再利用像素相連通處理法處理雜訊，分離出目標物與背景中的相似物。本系統經證實可完成水下目標物追蹤與估測的任務。

The objective of the thesis is to investigate the problems of the dynamic tracking and the states estimation of underwater object. The results of the research can contribute to the real-time detection of underwater object in a complex environment, by reducing the influence of light and the interferences from the background or foreground objects. In the experiment, after the image of target object was captured, we employed Kalman filtering technique to predict object's position at the next instant to enhance the image process speed. Then, by reducing the interferences from the surrounding objects with similar characters, the PTZ camera could automatically track the target by capturing the target in a square window and keeping it close the center area of the screen.
The research integrated the techniques of visual feedback, Kalman filter estimation and controlling of PTZ cameras, to achieve the abilities of dynamic underwater target tracking and estimation. The experimental results showed that the predicted position estimated by the Kalman filter would lead the corresponding measured position approximately. For the influence of a strong light source, we adopted the YCrCb color space to reduce the light effect. Image process techniques such as median filter, erosion and dilation, and connect component were integrated to deal with the influences of random noises, so as to separate the target from the background images. The system was proved to be effective in the underwater target tracking and estimation.

致謝 I
中文摘要 III
ABSTRACT IV
圖目錄 VIII
表目錄 X

第一章 緒論 1
1.1 前言 1
1.2 文獻回顧 5
1.3 研究動機與目的 8
1.4 論文架構 10
第二章 理論背景 11
2.1 數位影像處理(DIGITAL IMAGE PROCESSING) 11
2.1.1 RGB(Red Green Blue)圖像模型 12
2.1.2 色彩模型(Color Space) 13
2.1.3 灰階化（Gray Scale Manipulation） 17
2.1.4 影像二值化（Binarize Image） 18
2.1.5 中值濾波器（Median Filter） 19
2.1.6 型態學-膨脹（Dilation）、侵蝕(Erosion) 20
2.1.7 像素相連通(Connect Component)處理法 23
2.2 卡爾曼理論(KALMAN FILTER) 24
第三章 實驗流程與架構 31
3.1 硬體環境 33
3.1.1 防水PTZ攝影機 34
3.1.2 影像擷取卡 35
3.1.3 水下載具 (ROV) 35
3.2 軟體環境 36
3.2.1 影像擷取 38
3.2.2 PTZ控制系統 38
3.3 卡爾曼濾波器之運算流程 42
3.4 實驗方法 43
第四章 實驗結果與討論 45
4.1 影像擷取參數 45
4.2 實驗規劃與概述 46
實驗1:測試目標物向各個方向移動的狀況 47
實驗2:單純背景下目標物追蹤 49
實驗3:複雜背景環境下無卡爾曼濾波器估測 51
實驗4:複雜背景環境下加入卡爾曼濾波器估測 53
實驗5:目標物移動過遠導致失敗狀況 59
4.3 結果與討論 60
4.3.1 實驗討論與說明 60
實驗1 60
實驗2 60
實驗3 61
實驗4 61
實驗5 62
4.3.2 實驗誤差分析 62
第五章 結論與未來展望 67
5.1 結論 67
5.2 未來展望 68
參考文獻 69

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