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研究生:林建全
研究生(外文):Chien-ChuanLin
論文名稱:車用數位影像技術與應用之研究
論文名稱(外文):The Study of Techniques and Applications for Vehicular Digital Images
指導教授:王明習
指導教授(外文):Ming-Shi Wang
學位類別:博士
校院名稱:國立成功大學
系所名稱:工程科學系碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:87
中文關鍵詞:行車紀錄器數位浮水印眼睛偵測粒子濾波分類器眼睛狀態追蹤路標辨識模糊推論Adaboost 分類器支援向量機
外文關鍵詞:digital video recorderdigital watermarkingeye detectionparticle filtereye state trackingroad sign recognitionfuzzy inferenceAdaboost classifiersupport vector machine
相關次數:
  • 被引用被引用:1
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  • 下載下載:45
  • 收藏至我的研究室書目清單書目收藏:1
本論文旨在研究車用數位影像處理之技術與應用,即使用數位影像處理技術,應用於車用數位影像駕駛輔助與監控系統的研究,希望藉由本論文的研究能夠提供汽車司機更安全的駕駛輔助工具,以降低交通事故的肇事率。本論文的研究主題項目包括:(1) 行車紀錄器資料分析:播放行車紀錄器影像,顯示與分析行車資料。 (2) 道路標誌偵測與辨識:偵測並辨識前方道路警告與限制標誌,使司機更容易可以掌握前方道路的狀況。(3) 駕駛狀態監視與警示:監視並偵測司機的精神狀態,對處於疲勞駕駛狀態的司機提出警示,避免因駕駛疲勞所造成的交通事故。(4)鳥瞰式影像停車輔助:轉換車輛後方影像為鳥瞰式影像,輔助司機更容易判斷車輛後方的狀況及與障礙物的距離,而可更容易且安全地停靠車輛。(5) 前車偵測與防撞警示:偵測並判斷前方車輛的距離,預防與前車碰撞事故的發生。
首先,車輛的行車紀錄器記錄了一個正在運行的車輛的影片與相關資料,本行車紀錄器資料處理系統,處理有特定資料格式規範及資料安全保護的行車紀錄器資料。本系統可支援線上和離線瀏覽,線上瀏覽可即時顯示行車狀況及車輛所在位置,而離線瀏覽則無法即時顯示車輛所在位置,但是可進行事後行車狀況分析。對於資料的安全性,採用數位浮水印嵌入技術,以防止資料被篡改。影片播放器子系統可用於分析所記錄的數據,並可取得車輛發生交通事故前後的狀態。此行車紀錄器資料分析系統,不僅是一個簡單的行車紀錄器,也是一個高可信度與高安全性的系統。
在道路標誌偵測與辨識部分,採用兩個模糊推理方案執行影像前處理的工作。第一個模糊推理方案是檢查影像在檢查區域範圍的照度和紅色的變化,另一種模糊推理方案是檢查車輛的速度和方向盤角度的變化。在路標偵測方面,AdaBoost分類器用來檢測影像中候選路標的位置;而路標辨識方面採用支持向量機技術來識別候選路標的內容。所提出的路標偵測與辨識方法,不僅可以克服影像低照度的問題,及擁有過多紅色的背景問題,也提供高偵測率和高計算性能。
疲勞駕駛是交通事故的主要原因之一,本文提出一個車輛司機疲勞狀態檢測系統,以防止因司機疲勞駕駛所造成的交通意外。首先,在前處理階段,包括人臉檢測和眼睛的位置偵測,接著使用粒子濾波方法執行眼部位置及眼井狀態的追踪,最後執行駕駛疲勞檢測和監控,並對司機發出警告。
在像停車輔助的研究,提出一個反透式投影轉換法,將攝影機影像轉換成相對應的鳥瞰影像圖。本研究採用參數搜索演算法來取得所使用的影像轉換參數,本方法可以不必提供任何相機內部和外部的方位參數。對應轉換矩陣轉換法的使用可縮短影像轉換的處理時間,記錄第一張影格的相對應座標轉換成對應轉換矩陣,因之後的影格大小皆與第一張影格相同,且為相同攝影機所拍攝,因此,可直接使用對應轉換矩陣來進行影像轉換,可省卻座標轉換所需的計算量。對應轉換矩陣可以存儲為矩陣映射表,並加載到嵌入式平台進行影像座標轉換。根據實驗結果,所提出的方法可以提供一個更清晰,更準確的鳥瞰影像,來輔助車輛停車。
前方碰撞警告系統是基於上述的行車紀錄器系統的行車資料,前方碰撞警告系統整合影像邊緣偵測,車輛外圍偵測,特徵點匹配等方法。首先,根據車速資料設定影像所要偵測的區域範圍,在相鄰兩張影格中偵測特徵點及進行特徵點匹配。所提出的車輛外圍偵測方法,可過濾更多的雜訊並獲得較高的偵測率。
本研究的成果,可擴展應用於其他車用影像輔助系統,與先進智慧車、無人駕駛自動車系統結合,進而提供更安全舒適的行車環境。

This dissertation addresses five techniques and applications for vehicular digital images, namely the vehicular digital video recorder system, the road sign detection and recognition, the driver fatigue detection and monitoring, the top-view transformation model for image coordinate transformation, and the forward collision warning system.
The proposed vehicular digital video recorder system is active during vehicle movement and can securely store the data. The system supports an online real-time navigator and an offline video data viewer. The data viewer subsystem can be used to analyze the recorded data and obtain the status of the vehicle after a traffic accident.
Adaptive image pre-processing models are proposed for a road sign recognition system that uses two fuzzy inference schemes. The first scheme is used to check illumination changes and rich red color of a frame image by the checking areas. The other scheme is used to check vehicle speed and the angle of the steering wheel to determine the size and position of the detection area. The Adaboost classifier is employed to detect road sign candidates from an image and the support vector machine technique is employed to recognize the content of the candidate road signs. Mandatory and warning traffic signs are the targets. The proposed system, which overcomes the problems of low illumination and the rich red color around road signs, has a high detection rate and efficiency.
Driver fatigue is one of the main reasons for traffic accidents. A driver fatigue detection system is proposed. The preprocessing stage includes face detection and eye position extraction. In the second stage, eye tracking is performed for a sequence of frames and the eye state is determined using a particle filtering scheme. The final stage executes the fatigue detection and monitoring, and issues warnings.
The proposed top-view transformation model for image coordinate transformation involves transforming a perspective projection image into its corresponding bird’s eye view. A fitting parameters search algorithm estimates the parameters that are used to transform the coordinates from the source image. The interior and exterior orientation parameters of the camera are not required for this approach. The designed car parking assistance system can be installed at the rear end of the car, providing the driver with a clearer image of the area behind the car. Experimental results show that the proposed approaches provide a clearer and more accurate bird’s eye view.
A forward collision warning system based on the vehicular digital video recorder system and a vision sensor is proposed. The forward collision warning system integrates edge detection, vehicle bounding box detection, and feature point matching techniques. An initial detection region of interest is given in a frame along with the corresponding vehicle speed. Feature points are detected and matched in two adjacent frames. The proposed vehicle bounding box detection approach filters more noise than existing methods and has high detection accuracy.

Contents
摘要 I
Abstract III
致謝 V
Contents VI
List of Tables X
List of Figures XI
List of Abbreviations XIV
Chapter 1 Introduction 1
1.1 Vehicular Digital Video Recorder 1
1.2 Road Sign Detection and Recognition 2
1.3 Driver Fatigue State Detection and Monitoring 3
1.4 Top-View Transformation Model for Parking Assistant 3
1.5 Forward Collision Warning 4
1.6 Scope and Organization 5
Chapter 2 Related Works 6
2.1 Vehicular Digital Video Recorder System 6
2.2 Road Sign Detection and Recognition 6
2.3 Driver Fatigue State Detection and Monitoring 8
2.4 Top-View Transformation Model for Parking Assistant 10
Chapter 3 Proposed Approaches 12
3.1 Vehicular Digital Video Recorder 12
3.1.1 System Architecture 12
3.1.2 Hardware System – Embedded Platform 12
3.1.3 Software System - Data Viewer 14
3.1.4 Security Module 14
3.2 Road Sign Detection and Recognition 18
3.2.1 Fuzzy Adaptive Checking Area 18
3.2.2 Fuzzy Detection Area for Video Frames 23
3.2.3 Road Sign Detection Phase 26
3.2.4 Road Sign Recognition Phase 27
3.3 Driver Fatigue State Detection and Monitoring 29
3.3.1 Face Detection 30
3.3.2 Eye Position Extraction 33
3.3.3 Eye State Tracking 34
3.3.4 Eye States Monitoring and Fatigue Detection 38
3.4 Top-View Transformation Model for a Vehicle Parking Assistant 39
3.4.1 The Camera Model 40
3.4.2 Top-View Transformation Model 42
3.4.3 Optimal Searching Algorithm for TVTM Parameters 44
3.4.4 Implement TVTM into the Embedded Platform 47
3.5 Forward Collision Warning 48
3.5.1 System Architecture 48
3.5.2 Initial Region of Interest 49
3.5.3 Edge Detection 50
3.5.4 Vehicle Bounding Box Detection 51
3.5.5 Feature Matching and Vehicle Tracking 51
3.5.6 Forward Collision Warning 52
Chapter 4 Experimental Results and Discussions 53
4.1 Vehicular Digital Video Recorder 53
4.1.1 Experimental Results 53
4.1.2 Discussions 57
4.1.3 Summary 58
4.2 Road Sign Detection and Recognition 58
4.2.1 The Results of Static Images 59
4.2.2 The Results of Video Frames 62
4.2.3 Summary 65
4.3 Driver Fatigue State Detection and Monitoring 65
4.3.1 Experimental Results 66
4.3.2 The Fault Detection Cases Analysis 68
4.3.3 Performance Evaluation 68
4.3.4 Summary 69
4.4 Top-View Transformation Model for Vehicle Parking Assistant 69
4.4.1 Evaluation of TVTM 70
4.4.2 Frame Rate Estimation on the Embedded Platform 71
4.4.3 The Result Evaluation 72
4.4.4 Comparison with other IPM Methods 74
4.4.5 Discussions 74
4.4.6 Summary 75
4.5 Forward Collision Warning 75
4.5.1 Experimental Results 76
4.5.2 Summary 78
Chapter 5 Conclusions and Future Work 79
References 80
Publication List 86

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