臺灣博碩士論文加值系統

在提供使用者一個更精準標示目標的需求下，我們提出了一個整合式的物件追蹤系統。該系統是透過兩個不同的追蹤器而達到追蹤效果：其一是透過Adaboost結合像素為基底的種子特徵(seed features)來達到追蹤；透過這個追蹤器能夠精準地標示出追蹤目標。另一則是透過雙向K-means clustering的方法，輔以Adaboost結合以區塊為基礎的種子特徵來達到追蹤，並補足第一個追蹤器的不足。另外我們提供了讓使用者可以修正追蹤結果的工具；而透過所設計的可信指數量測，可以讓使用者輕鬆地決定是否該人工介入修正追蹤結果，例如是因為遮蔽事件發生或其他複雜情況發生的結果。

We proposed an integrated tracking system for applications which need more precise segmentation of target. The main tracking mechanism is accomplished by two trackers. The first tracker performs tracking by Adaboost on pixel-based seed features; it can provide more detailed segmentation of target. The second tracker achieves tracking by bidirectional k-means clustering on regions, and uses Adaboost on region-based seed features to provide compensations to the short the first tracker. We also implement a tool which allows users to refine the result manually. By confidence measure, users can easily choose the timing to interact with frames which probably are in occlusion or perform poorly due to some complex situation.

Content
Abstract 1
摘 要 iv
Content v
Chapter 1. Introduction 1
1.1. Tracking Issues 1
1.2. Target Representation 2
1.3. Feature Selection 3
1.4. Motivation 4
Chapter 2. Related Work and Background 5
2.1. Seed features 6
2.2. Adaboost 7
2.3. K-Means Clustering 9
Chapter 3. Proposed Method 11
3.1. Global-viewed Tracker 13
3.2. Local-viewed Tracker 20
3.2.1. Regionalization 21
3.2.2. Object Tracking with K-Means Clustering 25
3.2.3. Region-based Seed features 26
3.2.4. Region-Relation Judgment 27
3.3. Manual-Refinement Tool 28
3.4. Final Combination 30
3.4.1. Post-Process: Morphological Operations 30
3.4.2. Confidence Measurement 30
Chapter 4. Experimental Results 31
Chapter 5. Conclusion and Future Work 50
Reference 52

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