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系統識別號 U0026-1908201423075900
論文名稱(中文) 基於稀疏特徵擷取之粒子濾波器物件追蹤演算法
論文名稱(英文) Sparse-Based Object Tracking Using Particle Filter
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 吳品頡
研究生(英文) Pin-Jie Wu
學號 p76014216
學位類別 碩士
語文別 英文
論文頁數 43頁
口試委員 指導教授-連震杰
口試委員-林惠勇
口試委員-鄭銘揚
口試委員-戴顯權
口試委員-吳宗憲
中文關鍵字 視覺追蹤  稀疏表達  粒子濾波器  模板更新 
英文關鍵字 visual tracking  sparse representation  particle filter  template update 
學科別分類
中文摘要 稀疏表達在最近的追蹤演算法研究中是一項非常重要的技術,但是追蹤技術在現實生活中尚有許多需要克服的難題,例如遮蔽情況或者目標物的外型變化。在本篇論文裡,我們提出了一個以稀疏表達為基礎,能夠擷取全域性以及區域性特徵的追蹤方法。同時我們也提出一個完備的模板更新機制,能夠將目標物的變化情形持續更新到模板裡,對於全域性以及區域性的兩種模板都有獨立的更新機制。對於全域性的模板,我們使用兩個模板集合包含穩定模板以及普通模板,透過這兩種模板來獲得目標物變化的資訊,同時我們也會更新背景模板。對於區域性的資訊,我們利用更新一個由補釘影像所組成的字典來獲得最新的目標資訊。藉由這個完整的追蹤及模板更新機制,我們可以克服許多外型變化劇烈的追蹤任務。
英文摘要 Sparse representation is a significant technique in resent tracking research. However, there are many challenges in the real-world tracking task such as occlusion or appearance change. In this thesis, we propose a sparse-based tracking algorithm with global and local information. We also propose a robust template update scheme to catch the appearance variance. Two kinds of template are updated for global and local information independently. For global information, a stable template set and a normal template set are used to capture the appearance change. The background template set is also considered. For local information, a patches-based dictionary is updated in the tracking task. By the robust template update scheme, we can conquer serious appearance change in the tracking task.
論文目次 摘要 IV
Abstract V
誌謝 VI
Table of Contents VII
List of Tables IX
List of Figures X
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Related Work 2
1.3 System Flowchart 3
Chapter 2. Object Tracking Using Particle Filter 6
2.1 State Transition Model 8
2.2 Observation Model 11
2.3 Tracking Result 12
Chapter 3. Global and Local Feature Patch Modeling for Similarity Measure in Sparsity Base 14
3.1 Sparsity-Based Discriminative Classifier (SDC) 14
3.1.1 Templates Generation 14
3.1.2 Projection Matrix S creation for Discriminative Feature Selection 16
3.1.3 Confidence Measure 18
3.2 Sparsity-Based Generative Model (SGM) 19
3.2.1 Patch-Based Dictionary D Generation Based on Target Template at 1st Frame 19
3.2.2 Patch-Based Histogram Generation 20
3.2.3 Occlusion Handling 22
3.2.4 Similarity Function 23
3.3 Collaborative Model 25
Chapter 4. Target Template Updating 26
4.1 Discriminative Model Template Updating 26
4.1.1 Positive Template Updating 26
4.1.2 Negative Template Updating 28
4.2 Generative Model Template Updating 28
Chapter 5. Experimental Results 30
5.1 Evaluate Method 30
5.1.1 Precision Plot 30
5.1.2 Success Plot 30
5.1.3 Area Under Curve (AUC) 31
5.2 Robust Evaluate Scenarios 33
5.2.1 One-Pass Evaluation (OPE) 33
5.2.2 Spatial Robustness Evaluation (SRE) 33
5.2.3 Temporal Robust evaluation (TRE) 34
5.3 Test Data 36
5.4 Experimental Results and Data Analysis 37
Chapter 6. Conclusions and Future Work 39
6.1 Conclusion 39
6.2 Future Work 40
Reference 41
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