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研究生:岩子庭
研究生(外文):Tzu-Ting Yen
論文名稱:以影像為基礎之日間前車偵測與車距估算
論文名稱(外文):Vision-based Daytime Front-vehicle Detection and Distance Estimation
指導教授:陳敦裕陳敦裕引用關係
指導教授(外文):Duan-Yu Chen
口試委員:謝君偉魏志達康立威
口試委員(外文):Jun-Wei HsiehJyh-Da WeiLi-Wei Kang
口試日期:2016-6-24
學位類別:碩士
校院名稱:元智大學
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:2016
畢業學年度:104
語文別:英文
論文頁數:39
中文關鍵詞:車輛偵測陰影偵測對稱性偵測距離估算卷積神經網路
外文關鍵詞:vehicle detectionshadow detectionsymmetrical Speeded-UP Robust Features (SURF)distance estimationconvolutional deep neural network (CNN)
相關次數:
  • 被引用被引用:2
  • 點閱點閱:502
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  • 下載下載:105
  • 收藏至我的研究室書目清單書目收藏:0
城市的繁榮與否和交通的發展有密切的關係,人們的生活更與交通密不可分。便利的交通可以提升運輸效率,促進經濟活絡,讓人們有天涯若比鄰的感受,然而也伴隨著許許多多的交通事故,造成社會與家庭慘痛的損失。有鑑於此,各個先進的國家都致力於發展智慧型運輸系統(Intelligent Transportation System, ITS),交通運輸的安全性也是我們需要重視的一項課題。

本論文提出一個新的日間前車偵測與距離估算系統,由於在日間,在每台車輛底下一般皆可看得到陰影,因此我們以陰影作為車輛偵測的主要依據,偵測陰影後做出車輛位置的假設,當車輛間過近導致陰影無法偵測時,則以車輛的對稱性找出車輛可能的位置,最後再以深度學習中的卷積神經網路進行驗證,找出前方車輛的正確位置。在距離估算方面,以車輛在影像中之y座標於畫面中的比例做距離的估算。我們使用行車紀錄器錄製的影片來做實驗,實驗結果說明了我們提出的車輛偵測方法可有效的找出前方車輛位置。

A city is prosperous or not is close to the development of transportation and people’s lives are more closely linked with transportation. The convenience of traffic can enhance transport efficiency and make economy active. However, the transportation is along with many traffic accidents and results in the tragedies for family and society. In the light of this, a transport safety issue is what we need to focus on.

In this paper, we propose a novel daytime vehicle detection and distance estimation system. Because every vehicle has shadow beneath itself in the daylight condition, our vehicle detection is based on shadow detection to hypothesis vehicle’s probable locations. When the vehicle is too close to be detected by shadow, the symmetry of vehicle will be used to detect vehicles by symmetrical Speed-Up Robust Features (SURF) descriptor through a mirroring transformation. Finally, the hypothesized locations of the potential vehicles are verified using convolutional neural network (CNN) which is a kind of architecture in deep learning. In part of distance estimation, the y-coordinates of vehicles are used to calculate the proportions in input image and we use the proportions to estimate the distances between host vehicle and front vehicles. Experimental results show the strong performance of our proposed system.

摘 要
Abstract
誌 謝
Table of Contents
List of Figures
List of Tables
Chapter 1. Introduction
Chapter 2. Proposed Method
2.1 Overview
2.2 Shadow Detection for Upper Half Image
2.3 Shadow Detection for Lower Half Image
2.4 Symmetrical SURF for Vehicle Detection
2.5 Convolutional Neural Network
2.6 Distance Estimation
Chapter 3. Experimental Results
3.1 Overview
3.2 Time complexity analysis
3.3 Front Vehicle Detection
3.4 Distance Estimation
Chapter 4. Conclusion
References


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