博碩士論文 102522012 詳細資訊




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姓名 梁振浩(Cheng-hao Liang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 利用光流與加速穩健特徵作車輛距離估測
(Vehicle Distance Estimation Using Optical Flow and Speed Up Robust Feature)
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摘要(中) 因高速公路的事故率日益嚴重的影響,使得車輛防碰撞系統為目前各個車廠積極地開發的趨勢,不僅如此,目前車輛防碰撞系統也被廣泛應用在無人駕駛車上,例如Apple、Benz、BMW、Audi等等開發廠商中,以 Google全自動駕駛汽車為具代表性。
近年來,大部分的智慧型防碰撞系統都搭配感測器來預防碰撞發生,也因為目前感測器價格上相當昂貴且普遍民眾對此消費需求不高,讓低成本的防碰撞系統更為廠商不可或缺的考量。本篇論文主要目標為使用單鏡頭攝影機及無任何感測器的輔助下,當SURF對稱線及車底特徵兩者條件都符合者即為車輛區塊,得知區塊後再透過影像校正後參數換算,判斷與前方車輛距離是否過於相近。藉由光流運動方向的位移量以及車道線資訊,來判斷鄰近車輛是否快速切入本車道與駕駛者是否有偏離車道的情況,若有則警惕駕駛者以策安全。
在實驗方面可以分為三大部分來探討,分別為候選車輛區塊偵測、車輛區塊篩選與行為判斷、車輛區塊追蹤。實驗結果顯示,於特定氣候環境下,本研究方法也有良好偵測效果。
摘要(英) Because effect of accident rate rising on the highway, making vehicle anti-collision system as the current main trends. Moreover, currently vehicle anti-collision system is also widely used in unmanned aerial vehicles. Such as Apple, Benz, BMW, Audi, etc. Among Google Driverless Car as a representative.
In recent years, most of the vehicle anti-collision system with sensors to prevent collisions. The reason the current prices of sensors are still expensive and the consumer demand of people is not high. Then, making low-cost vehicle anti-collision system to be vendors essential considerations.
The purpose of the paper is to use single camera without assisting sensors to detect vehicle. Then, both symmetric line and the bottom of vehicle are met condition as vehicle. After getting the location of vehicle, we can use the information of vehicle and look-up table to convert the real distance. And then we can determine whether the forward vehicle is too close. By using both the strong direction of optical flow and the information of lane detected, we can determine whether vehicle departure lane and the near vehicle quickly drive to own lane. Then, if is true to alert driver. Avoiding accident to ensure safety of driver.
Three different experiments were conducted to verify the validity of our proposed method. They were categorized in terms of candidate vehicle detection, candidate vehicle filter and judge, vehicle tracking. Experimental results demonstrate that the proposed method exhibit better detection rate.
關鍵字(中) ★ 車輛偵測
★ 陰影
★ 車道線
★ 光流
★ SURF
關鍵字(英) ★ vehicle detection
★ shadow
★ lane
★ optical flow
★ SURF
論文目次 摘要 i
Abstract ii
誌謝 iii
圖目錄 vii
表目錄 ix
演算法目錄 ix
第一章 緒論 1
1.1 研究動機 1
1.2 論文流程 5
1.3 論文架構 8
第二章 相關研究 9
2.1 車道線偵測 9
2.2 前車偵測 10
2.2.1 外貌為基礎的偵測(Appearance-based) 11
2.2.2 學習為基礎的偵測(Learning-based) 16
第三章 系統流程與演算法 17
3.1 影像校正 17
3.2 車底偵測 18
3.2.1 決定ROI 18
3.2.2 偵測及預估車輛大小 19
3.3 SURF為基礎之對稱線偵測 21
3.3.1 加速穩健特徵 (SURF) 22
3.3.2 利用Multi-Resolution尋找SURF對稱點及配對 28
3.3.3 標示車輛對稱線 30
3.3.4 標示車輛區塊 31
3.4 前車距離偵測 32
3.4.1 自適應增強演算法(Adaboost Algorithm) 34
3.4.2 單一迴積特徵 36
3.4.3 層級分類器(Cascade classifier) 37
3.5 霍夫轉換之車道線偵測及偏離判斷 38
3.6 光流追蹤(Optical Flow) 41
3.6.1 影像金字塔 (Image pyramid representation) 41
3.6.2 對車輛區塊追蹤及判斷 42
第四章 實驗結果與討論 44
4.1 實驗環境 44
4.2 結果與討論 45
4.2.1 車輛偵測 48
4.2.2 車牌偵測 50
第五章 結論與未來研究方向 51
5.1 結論 51
5.2 未來研究方向 52
參考文獻 53
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指導教授 范國清(Kuo-Chin Fan) 審核日期 2015-7-24
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