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研究生:陳涵軒
研究生(外文):Han-Hsuan Chen
論文名稱:整合外觀與邊緣特徵之路面夜間機踏車偵測
論文名稱(外文):Integrating Appearance and Edge Features for on-road Bicycle and Motorcycle Detection in the Nighttime
指導教授:傅立成傅立成引用關係
口試委員:洪一平方瓊瑤黃世勳
口試日期:2014-07-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
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:2014
畢業學年度:102
語文別:英文
論文頁數:68
中文關鍵詞:機踏車偵測機踏車騎士偵測夜間近紅外線空間關係
外文關鍵詞:On-road Bicycle and Motorcycle DetectionCyclist DetectionFeature IntegrationSpatial RelationshipNighttime
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在智慧型車輛系統上,障礙物偵測一直是重要的議題,行人因其脆弱性,吸引許多專家進行相關研究。除了行人偵測外,機踏車也是一種重要的障礙物偵測。在過去的文獻中,大多使用整體式機踏車偵測,然而機踏車的寬高比在不同視角的差異,會造成整體式機踏車偵測無法取得好結果。尤其在夜間環境下,光源不如日間明亮,更會造成部分機踏車外觀不清楚。因此本篇論文提出一個在移動式車輛上使用之基於近紅外線光源的夜間路面機踏車偵測方法,在近紅外線影像中會顯示輻射近紅外線以及反射經由近紅外線投射燈投出的紅外線的區域。為了解決機踏車的寬高比、視角差異,本篇論文引入分組式組件的偵測方法,使用兩種影像特徵分組擷取,結合這些特徵在影像上與影像中心的相對位置資訊。
除了機踏車偵測外,騎著機踏車的人也需要偵測。因為機踏車駕駛姿勢固定,我們採用梯度方向直方圖當成特徵的機踏車駕駛偵測。然而道路有許多物品外觀與機踏車駕駛相似,如路燈、樹等,因此會產生許多錯誤偵測。因機踏車駕駛與其騎乘之機踏車空間關係十分顯著,可以用來降低錯誤偵測。本篇論文提出空間關係改善機踏車駕駛偵測,使用駕駛與機踏車的質心間位移間大小比例來當成所用之空間關係,分析此空間關係的分佈發現可以呈現高斯分佈,利用高斯分佈的機率公式來計算其機率,最終結果再過濾掉可能性低的組合。因為空間認證效果取決於偵測物體的視窗準確性,而分組式機踏車偵測容易造成視窗的浮動,提出視窗修正方法進行修正原本偵測到的視窗,進而提升空間關係認證效果。


It is critical to detect bicycles and motorcycles on the road because collision of autos with those light vehicles becomes major cause of on-road accidents nowadays especially in the nighttime. Therefore, a vision-based nighttime on-road bicycle and motorcycle detection method relying on use of a camera and near-infrared lighting mounted on an auto vehicle is proposed in this paper. Generally, the objects will reflect near-infrared lighting. However, some components of the bicycles and the motorcycles absorb most infrared lighting and thus make the bicycles and motorcycles hardly recognizable. To cope with this problem, the aforementioned detection method is part-based, which combines the two kinds of features related to the characteristics of bicycles and motorcycles. Also, the information about the geometric relation among all the parts and the object centroid is learned off-line. Due to high computation load, selection of effective parts with better geometric information is imperative for detection.
On the other hand, cyclist is also an important object to detect. We adopt a two-fold strategies, where one detects the cyclist by a holistic-based detector, and second is to establish a spatial relationship model between the cyclist and his/her riding vehicle off line. In particular, the second strategy filters out the wrong detection. The performance of spatial relationship validation depends on the tightness of bounding box. Hence, we propose a bounding box refinement to refine the detection results. To validate the proposed results, several experiments are conducted to show that the developed system is reliable in detecting bicycles and motorcycles on the road in the nighttime.


口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vii
LIST OF TABLES x
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Challenges 3
1.3 Related Work 6
1.4 Contributions 9
1.5 Thesis Organization 10
Chapter 2 Preliminaries 12
2.1 Histogram of Oriented Gradient (HOG) 12
2.1.1 HOG Descriptor 12
2.1.2 HOG Feature Encoding 14
2.2 Support Vector Machine (SVM) 14
2.2.1 Objective of SVM 15
2.2.2 Linear SVM 16
2.2.3 Soft Margin 18
2.3 System Overview 20
Chapter 3 Near-Infrared Part-based Classifier Construction 22
3.1 Part Extraction 23
3.1.1 Edge-based Feature Part Extraction 23
3.1.2 Appearance-based Feature Part Extraction 24
3.2 Part Clustering 26
3.2.1 Similarity Measure for Edge-Based Feature 26
3.2.2 Similarity Measure for Appearance-Based Feature 27
3.3 Exploiting Relationship among Parts and Object Centroid 30
3.3.1 Position Relation Model Construction 30
3.3.2 Centroid Information Clustering 32
3.4 Part Selection 33
Chapter 4 Near-Infrared based Nighttime Bicycle and Motorcycle Detection 36
4.1 Overview of Detection System 37
4.2 Preprocessing 38
4.3 Bicycle and Motorcycle Detection 40
4.3.1 Part Detection and Centroid Voting 40
4.3.2 Centroid Finding by Analyzing the Voting Space 41
4.4 Bounding Box Refinement 42
4.4.1 Bounding Box Extending 42
4.4.2 Bounding Box Mixing 43
4.5 Cyclist Detection with Spatial Relationship Model 44
4.5.1 The observation of Cyclist and Vehicle 44
4.5.2 Spatial Relationship Model 45
4.5.3 Cyclist Detection Refinement with Spatial Relationship Model 48
Chapter 5 Experiments 49
5.1 Environment Setting and Performance Measurement 49
5.1.1 Environment Setting 49
5.1.2 Performance Measurement 50
5.2 Training Dataset 52
5.3 Experiment Results of Caltech Motorbike Dataset 53
5.4 Comparison of the Part-based Feature Integrating 55
5.5 Experiment Results of nighttime on-road Bicycle/Motorcycle and Cyclist Detection 58
5.5.1 Performance of nighttime on-road Bicycle/Motorcycle Detection 58
5.5.2 Performance of nighttime Cyclist Detection 60
Chapter 6 Conclusion 63
References 65


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