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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题名:	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
作者:	梁振浩;Liang,Cheng-hao
贡献者:	資訊工程學系
关键词:	車輛偵測;陰影;車道線;光流;SURF;vehicle detection;shadow;lane;optical flow;SURF
日期:	2015-07-24
上传时间:	2015-09-23 14:25:44 (UTC+8)
出版者:	國立中央大學
摘要:	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。 ;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.
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