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研究生:王瑞鴻
研究生(外文):Wang, Ruei Hong
論文名稱:強健式視覺追蹤應用於擴增實境之研究
論文名稱(外文):Robust visual tracking for augmented reality
指導教授:何瑁鎧何瑁鎧引用關係
指導教授(外文):Hor, Maw Kae
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
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:2011
畢業學年度:99
語文別:中文
論文頁數:66
中文關鍵詞:擴增實境視覺追蹤立體視覺剛體運動
外文關鍵詞:Augmented realityvisual trackingstereo visionrigid body motion
相關次數:
  • 被引用被引用:1
  • 點閱點閱:287
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
視覺追蹤(visual tracking)一直是傳統電腦視覺研究中相當重要的議題,許多電腦視覺的應用都需要結合視覺追蹤的幫助才能實現。近年來擴增實境(augmented reality)能快速成功的發展,均有賴於視覺追蹤技術上之精進。擴增實境採用視覺追蹤的技術,可將虛擬的物件呈現在被追蹤的物體(真實場景)上,進而達成所需之應用。

由於在視覺追蹤上,被追蹤之物體易受外在環境因素影響,例如位移、旋轉、縮放、光照改變等,影響追蹤結果之精確度。本研究中,我們設計了一套全新的圖形標記方法作為視覺追蹤之參考點,能降低位移、旋轉與光照改變所造成追蹤結果的誤差,也能在複雜的背景中定位出標記圖形的正確位置,提高視覺追蹤的精確度。同時我們使用立體視覺追蹤物體,將過去只使用單一攝影機於二維影像資訊的追蹤問題,提升至使用三維空間的幾何資訊來做追蹤。然後透過剛體(rigid)特性找出旋轉量、位移量相同的物件,並且結合一致性隨機取樣(random sample consensus)之技巧以估測最佳的剛體物件運動模型,達到強健性追蹤的目的。

另外,我們可由使用者提供之影片資訊中擷取特定資料,透過建模技術將所產生之虛擬物件呈現於使用者介面(或被追蹤之物體)上,並藉由這些虛擬物件,提供真實世界外之資訊,達成導覽指引(或擴增實境)的效果。

實驗結果顯示,我們的方法具有辨識時間快、抗光照變化強、定位準確度高的特性,適合於擴增實境應用,同時我們設計的標記圖形尺寸小,方便適用於導覽指引等應用。

Visual tracking is one of the most important research topics in traditional computer vision. Many computer vision applications can not be realized without the integration of visual tracking techniques. The fast growing of augmented reality in recent years relied on the improvement of visual tracking technologies.
External environment such as object displacement, rotation, and scaling as well as illumination conditions will always influence the accuracy of visual tracking. In this thesis, we designed a set of markers that can reduce the errors induced by the illumination condition changes as well as that by the object displacement, rotation, and scaling. It can also correctly position the markers in complicated background to increase the tracking accuracy. Instead of using single camera tracking in 2D spaces, we used stereo vision techniques to track the objects in 3D spaces. We also used the properties of rigid objects and search for the objects with the same amount of rotation and displacement. Together with the techniques of random sample consensus, we can estimate the best rigid object motion model and achieve tracking robustness.
Moreover, from the user supplied video, we can capture particular information and then generate the virtual objects that can be displaced on the user’s device (or on the tracked objects). Using these techniques we can either achieve navigation or guidance in real world or achieve augmented reality as we expected.
The experimental results show that our mechanism has the characteristics of fast recognition, accurate positioning, and resisting to illumination changes that are suitable for augmented reality. Also, the size of the markers we designed is very small and good for augmented reality application.

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1動機和目的 1
1.2問題描述 2
第二章 相關研究 4
2.1視覺追蹤 4
2.2擴增實境 6
2.2.1標記系統 7
2.2.2無標記系統 13
第三章 背景知識 17
3.1尺度不變特徵轉換 17
3.2一致性隨機取樣方法 18
3.3零平均正規化相關匹配法 21
3.4相機校正 22
第四章 標記圖形設計 24
第五章 視覺追蹤方法 27
5.1標記系統擴增實境 28
5.1.1偵測標記圖形 29
5.1.2尋找對應標記圖形 33
5.1.3計算三維座標 34
5.1.4追蹤目標物 37
5.1.5產生虛擬物件 39
第六章 實驗結果 41
6.1無標記系統影像追蹤實驗 41
6.2使用多個標記圖形擴增實境實驗 44
6.3標記系統屬性測試 49
6.3.1標記圖形可視範圍實驗 49
6.3.2標記圖形角度可視範圍實驗 53
6.3.3標記圖形光照可視範圍實驗 54
6.3.4標記圖形辨識時間測量實驗 55
6.3.5標記圖形計算三維空間距離實驗 56
6.4標記圖形立體視覺與使用單一相機視覺比較 58
第七章 結論 61
未來研究 62
參考文獻 64

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