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研究生:呂墩棖
研究生(外文):LU, TUN-CHENG
論文名稱:應用粒子濾波器與最佳估測技術於目標追蹤系統
論文名稱(外文):Applying Particle Filter and Optimal Estimation Technology to Object Tracking System
指導教授:鍾翼能鍾翼能引用關係
指導教授(外文):Chung,Yi-Nung
口試委員:鍾翼能陳雍宗魏忠必王中行許釗興
口試委員(外文):Chung,Yi-NungChen,Yong-Zong Wei,Zhong-BiWang,Zhong-XingXu,Zhao-xing
口試日期:2017-06-14
學位類別:博士
校院名稱:國立彰化師範大學
系所名稱:電機工程學系
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:2017
畢業學年度:105
語文別:英文
論文頁數:48
中文關鍵詞:卡門濾波器粒子濾波器目標追蹤系統影像處理技術
外文關鍵詞:Kalman filterparticle filtertarget tracking systemimage processing
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摘要
監視系統以及數位影像處理技術日益普及,即時監視系統成為重要的研究目標。本論文提出應用粒子濾波器與最佳估測技術於目標追蹤系統,並以影像處理技術作為量測方法,最佳估測技術則應用卡門濾波器,在影像追蹤時結合卡門濾波器及粒子濾波器技術, 本論文需要許多的影像處理技術做前置處理。在一般視覺影像下,影像很容易因為光照變化的影響,而改變目標的色彩特徵,本論文提出利用色彩空間轉換的方法,將原先RGB色彩空間轉換為HSV色彩空間,利用HSV色彩空間的特性以降低陰影的干擾,及光照變化所產生的影響。本文亦提出以動態調整偵測區域的具適應性濾波器方法來量測各種不同條件下的目標追蹤,實驗結果證實本文所提出的方法具有足夠的準確度,其適應性可以克服目標追蹤的問題,本研究所設計的追蹤系統可提升量測的準確度。
Because the surveillance system and the digital image processing technology is more popular today, therefore the real-time surveillance system has become an important research topic. In this dissertation, the particle filter algorithm and optimal estimation technology are applied to object tracking system. The system also uses image processing technology to process the object image. The Kalman filter is applied to make optimal estimation. This study combines some algorithms which include Kalman filter, particle filter, and image processing for solving object estimation and tracking problems. A set of image sequences from video views are applied to evaluate performance. In general, the color characteristic of image is easy to change because of the impact of illumination. Therefore, this dissertation proposed a method by using the color space transformation. In order to reduce the effect of shadow and impact of changing illumination, the RGB color space is transferred to HSV color space in this algorithm. Based on the experimental result, it indicates the proposed approach successfully tracks objects and the performance efficiency can be enhanced by using the proposed algorithm.
摘要 i
ABSTRACT ii
ACKNOLOGYMENT iii
TABLE OF CONTENTS iv
LIST OF FIGURES v

CHAPTER 1 INTRODUCTION 1
1. 1 Background and motivation 1
1. 2 Research contributions 3
1. 3 Dissertation contents 3
CHAPTER 2 IMAGE PROCESSING TECHNOLOGY 5
2. 1 Color space 6
2. 2 Image binarization 9
2. 3 Morphological algorithm 11
2. 4 Image filter 17
CHAPTER 3 OPTIMAL ESTIMATION ALGORITHM AND PARTICLE FILTER 19
3. 1 Optimal estimation algorithm 19
3. 2 Particle filter 23
CHAPTER 4 EXPERIMENTAL RESULTS AND ANALYSIS 29
4. 1 Object extract 29
4. 2 Experiment results 33
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 39
5. 1 Conclusions 39
5. 2 Future work 39
REFERENCES 41
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