以運動補償模型為基礎之移動式平台物件追蹤

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姓名

呂健益(Jian-Yi Lu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

以運動補償模型為基礎之移動式平台物件追蹤
(Object Tracking Using Motion Compensation Based Motion Model for Mobile Cameras)

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摘要(中)

電腦視覺在移動平台上的應用日趨發達，其中物件追蹤在手機等移動平台的應用中扮演重要角色。在移動平台上追蹤物件困難點在於，畫面中不僅有目標物件在移動，同時背景也不停變化，物件可能出現的範圍比靜止平台的物件追蹤要大上許多。當物件在前後畫面位置差距大時，對於物件移動的預測就顯得格外重要。因此，本論文提出適用在移動平台追蹤上的運動補償模型，透過粒子濾波器(PF)，估測出補償相機移動量後之物件在二維影像中的運動量。粒子濾波器利用相機和物件的補償相機移動量後之物件在二維影像中的運動量，估測物件在二維影像上的運動量。相機的運動量，則使用SURF演算法計算。實驗結果顯示，我們提出的追蹤演算法應用在移動式平台上時，對於移動快速或不規則運動的物件皆能有良好的追蹤效果。

摘要(英)

Robustness of visual tracking on mobile devices plays a key role in the success of emerging applications of robotics and cell phones. The prediction accuracy of object motion makes a great impact on the tracking accuracy. Therefore, this paper proposes to combine motion compensation and motion model to achieve fast and accurate estimation of object motion in 2-D images. At each time instant, the particle filter estimates the 2D object motion after motion compensation in the image based on the information of camera motionCamera motion is extracted with the aid of Speed-Up Robust Feature (SURF) algorithms. With our tracking algorithm, accurate tracking on mobile platforms can be achieved with a small number of particles. The experimental results show that our proposed tracking algorithm on mobile platforms performs well even if the rapid and irregular object motion exists.

關鍵字(中)

★ 運動補償模型
★ 移動平台
★ 物件追蹤
★ 粒子濾波器

關鍵字(英)

★ particle filter
★ motion model
★ mobile cameras
★ Visual tracking

論文目次

摘要 ii
Abstract iii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 研究方法 3
1.4 論文架構 3
第二章貝式估測器 3
2.1 貝氏濾波器(Bayesian Filter) 4
2.2 卡爾曼濾波器(Kalman Filter) 6
2.3 擴展式卡爾曼濾波器(Extended Kalman Filter) 8
2.4 粒子濾波器(Particle Filter) 9
2.5 以色彩為基礎的粒子濾波器運用在物件追蹤 11
2.5.1 運動模型(Motion Model) 11
2.5.2 量測模型(Measurement Model) 12
2.5.3 色彩模型更新與再取樣程序 14
2.6 總結 15
第三章基於移動平台之物件追蹤技術現況 16
3.1 經由偵測的追蹤(Tracking-by-Detection) 16
3.2 運動補償為基礎的物件追蹤 19
3.3 總結 23
第四章以運動補償為基礎的運動模型之物件追蹤系統 24
4.1 系統架構 24
4.2 以Speed-up Robust Feature為基礎計算全域運動特徵 28
4.3補償相機移動量後之物件在二維影像中的運動量的初始值 30
4.4 以運動補償模型估測補償相機移動量後之物件在二維影像中的運動量 32
4.5 運動模型的可調式高斯雜訊 34
4.6 結論 35
第五章實驗結果與討論 36
5.1 實驗參數與測試影片 36
5.2.1 追蹤系統的準確度 37
5.2.2 追蹤系統的強健度 39
5.2.3 粒子權重差異度 40
5.2.4 系統計算複雜度 43
5.3 總結 43
第六章結論與未來展望 44
參考文獻 45

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2010-7-27

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