臺灣博碩士論文加值系統

近年來，有關駕駛輔助系統的相關研究都是一個熱門的話題。原因除了擁有廣大的市
場需求外，其所含括的應用範圍也相當的廣泛，包括車道偏離警示，碰撞預警，行人
和車輛偵測系統等等。本文提出了一套即時車輛偵測和追蹤系統，能夠偵測駕駛人視
野正前方的車輛。該系統主要有兩個核心技術:使用Adaboost 演算法訓練分類器用以
偵測道路上的所有可能車輛物件；再使用對稱軸檢測與水平垂直邊緣分析方法來判斷
該物件是否為車輛。經過各種背景環境條件不同下影片的測試結果，證明本系統是具
有相當大的可行性的。

Recently, extensive research concerning driver assistance systems has been conducted. Related
applications include lane departure warning, trac sign recognition, and pedestrian
and vehicle detection systems. This paper presents a real-time vehicle detection and tracking
system capable of detecting the vehicles in front of a vehicle. The system consists of 2
main steps: generating candidates with respect to a vehicle by using the AdaBoost learning
algorithm and verifying the candidates according to symmetry measurement and horizontal
and vertical edge analysis. The proposed system is proven to be eective in various trac
scenarios through experiments.

Abstract i
Contents ii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Description of approach 6
2.1 System overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Vehicle Detection with Adaboost Algorithm . . . . . . . . . . . . . . . . . . 8
2.2.1 Adaboost Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 The Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.3 The Training Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.4 Integral Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.5 Cascade Classier . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
ii
2.3 Symmetry Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 vehicle candidate selection . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Image Preprocess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.3 Symmetry Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Horizontal and Vertical Edge Analysis . . . . . . . . . . . . . . . . . . . . . 18
2.4.1 Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5 Scene Judgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Experimental Result 22
3.1 Vehicle Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Vehicle Candidate Judgment . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

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