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研究生:謝孟修
研究生(外文):Meng-Hsiu Hsieh
論文名稱:電腦視覺於機車相對距離之空間呈現
論文名稱(外文):The Spatial Representation of the Computer Vision Based Motorcycle Relative Distance
指導教授:陳柏華陳柏華引用關係
口試委員:陳俊杉陳柏翰謝佑明
口試日期:2015-06-29
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:45
中文關鍵詞:電腦視覺方向梯度直方圖支持向量機卡曼濾波機車交通安全
外文關鍵詞:Computer VisionHistogram of Oriented GradientSupport Vector MachineKalman FilterMotorcycleTraffic Safety
相關次數:
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機車在東南亞地區不僅常見且為高使用率之運輸工具,因其高機動性及便利停放之特性,機車持有比在我國居高不下。然而,機車騎士暴露於車體外之比例高,且常在車流中鑽行以及騎士之車道觀念較薄弱的情況下,造成機車之安全程度較其他運具低。因此本研究希望透過電腦視覺,自動觀察特定路段或路口之機車微觀駕駛行為,並擷取巨觀車流之資料。
在本研究中,攝影機架設於車道上方,經由機車之俯視畫面偵測路面中之機車並取得位置。方向梯度直方圖以及支持向量機被用來偵測錄影機所錄畫面中機車之位置,並應用卡曼濾波及匈牙利演算法,串聯機車之軌跡。
透過本研究的自動化系統,取得車流之資訊,其中包括(1)在特定車道或路口之機車軌跡,(2) 機車之交通流量及平均速度,(3)縱向及橫向之機車相對空間累積位置圖之特徵分析,(4)相對速度快之機車及相對速度慢之機車與周邊機車相對空間位置圖之特徵分析。本研究透過真實之拍攝影片,驗證路段之機車安全指標,此研究之延伸,可能被運用於機車安全之路面幾何危險偵測。


Individual motorcycle behavior depends on the surrounding environment such as traffic flow and the geometric design of the road. For their own safety, motorcyclists react to the distance and velocity of other vehicles. Especially during rush hours, the traffic condition changes rapidly. By setting up a camera at the road intersection to record traffic flow videos, the relative spatial position and the motorcycles’ direction angle are measured by analyzing the data. The motorcycle behavior at the intersection is observed and analyzed through computer vision and image processing methods. The Histogram of Gradients (HOG) descriptor is adapted for the detection of motorcycles utilizing a Support Vector Machine (SVM), and the Kalman Filter is employed for the tracking of the motorcycles’ trace. Through this approach, we observe the motorcycles’ traffic flow and traffic characteristics such as: (1) motorcycle trajectories on specific road section, (2) motorcycle traffic volume and average speed, (3) the pattern of accumulated relative spatial position of all the motorcycles from the video along the horizontal axis and vertical axis, and (4) the pattern of accumulated relative spatial positions of relatively fast and relatively slow motorcycles from the video. We have taken real world videos for the validation of the proposed approach. The results of this study could serve as a reference for traffic safety guidance for motorcyclists and could potentially be applied for detection of danger road geometries.

口試委員會審定書 i
致謝 ii
摘要 iv
Abstract v
Table of Contents vi
List of Figures viii
List of Tables x
Chapter 1 Introduction 1
1.1 Background 1
1.2 Objective of the research 2
1.3 Scope of the research 2
1.4 Structure of the research 2
Chapter 2 Literature Review 4
2.1 Detection Descriptor and Image Processing 4
2.2 Tracking Algorithm 5
2.3 Traffic Parameter Collection and Traffic Pattern Observation 6
2.4 Summary of Literature Review 7
Chapter 3 Methodology 8
3.1 Preprocessing Steps 9
3.1.1 Camera Calibration 9
3.1.2 Positive and Negative Samples Collection 9
3.1.3 HOG Feature Calculating and SVM Training 10
3.1.4 Region of Interest Segmentation 11
3.1.5 Coordinate Transformation 14
3.2 Motorcycle Recognition Steps 15
3.3 Tracking Steps 16
3.4 Safety Space of Motorcycles 20
Chapter 4 Experimental Result 23
4.1 Performance of detector 23
4.2 Performance of track 28
4.3 Relative Spatial Position 31
Chapter 5 Conclusions and Future Work 39
5.1 Conclusion 39
5.2 Future Work 41
Reference 43


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