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研究生:鍾書銘
研究生(外文):Shu-Ming Chung
論文名稱:以SOPC實現強健性彩色影像物體追蹤及移動預測系統
論文名稱(外文):Using SOPC to Implement Robust Color Image System for Object Tracking and Motion Estimation
指導教授:楊明興楊明興引用關係
指導教授(外文):Ming-Shing Young
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:81
中文關鍵詞:權重值感興趣區域移動預測SOPC
外文關鍵詞:SOPCweight valuemotion estimationROI(Region of Interest)
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  本研究利用高整合性的SOPC系統結合彩色影像追蹤模組,實現一個強健性的物體追蹤及預測系統。其主要透過對二值化的視訊畫面進行分割,得到M x N的區塊與各區塊X、Y的位址定位。並將每區塊中二值化得到的數值累加,得到該區塊分數。將每個區塊不同的分數除以總分,定義得到的值為各區塊的權重值。並將每個區塊不同的權重值與區塊位址相乘並加總,可計算得追蹤物體的中心位置。
  系統中亦規劃感興趣區域的範圍,對於追蹤物體劃分出一個矩形區域邊界。利用感興趣區域的規劃,可結合影像壓縮技術使得應用在監視系統時,減少影像儲存量,降低監視系統建構成本。移動預測功能則可結合主動式攝影機;透過預測的移動趨勢,將追蹤目標物維持在影像畫面的中心,以達到最佳追蹤及紀錄效果。
  This research utilized the highly integrated SOPC to combine the color tracking module so as to implement a robust object tracking and motion estimation system. First, it would divide the binary image into M×N blocks matrix and define positions of X, and Y of each block. Then, this research would use accumulator to add up the binary value of every block and it would regard it as the scores for each block. After that, it would divide these scores by the total score of current frame. Then, the result could be defined as the weight value of all blocks. The sum of these weight values that were multiplied by blocks’ position is the actual location of the tracking object.
  In this system, it also schemed out a rectangular region as the ROI of the tracking object. According to the result, it suggested that it would be better to process the ROI cooperate with novel image compression technology, JPEG200 or MPEG4 in the future. The storage capacity could be reduced for image and the surveillance system’s price could be cost down. The function of motion estimation can be used with active CCD camera. By the predicted trend of the tracking object, the object could be kept in the center of image and the best record and tracking could be achieved.
第一章 緒論 1
1-1 研究動機與目的 1
1-2 內文架構 2
第二章 系統的基礎原理 4
2-1 彩色目標物的分離 4
2-1-1顏色空間簡介 5
2-1-2顏色分離系統的設計與規劃 7
2-2 權重式追蹤演算法 12
2-3 感興趣區域的定義 14
2-4 物體移動趨勢預測 17
第三章 系統設計 19
3-1 系統硬體架構 19
3-1-1 彩色CCD攝影機規格 19
3-1-2 NTSC視訊輸入A/D轉換電路 20
3-1-3 SOPC系統 22
3-1-3-1 SOPC發展版簡介 23
3-1-3-2 視訊處理模組 24
3-1-3-3 Nios處理器 35
3-1-3-4 SOPC系統的整合 38
3-1-4 NTSC視訊輸出D/A轉換電路 42
3-1-5 I2C控制電路 43
3-2 系統軟體設計 45
3-2-1 Nios的中斷副程式的規劃 45
3-2-1-1 Nios的中斷副程式 45
3-2-1-2 系統中斷副程式的設計 48
3-2-2 Nios使用者指令集的規劃 49
3-2-2-1 Nios的使用者指令集 49
3-2-2-2 系統中使用者指令集的運用 51
3-2-3 系統程式的規劃與運作 52
第四章 系統測試與結果 57
4-1 設定不同RGB容許範圍的比較 57
4-2 感興趣區域的設定結果驗證 60
4-3 物體追蹤及預測結果 63
第五章 問題討論 71
5-1 SOPC發展版訊號的衰減問題 71
5-2 視訊輸出訊號的遮罩問題 71
5-3 提高解析度的評估 74
第六章 結論與未來展望 76
參考文獻 78
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