利用隱式型態模式之高速公路前車偵測機制 ; A Highway Preceding Vehicle Detection Scheme By Using Implicit Shape Model

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/44757

Title:	利用隱式型態模式之高速公路前車偵測機制;A Highway Preceding Vehicle Detection Scheme By Using Implicit Shape Model
Authors:	阮門督;Minh-Duc Nguyen
Contributors:	資訊工程研究所
Keywords:	車偵測;Implicit Shape Model;Vehicle detection
Date:	2010-07-30
Issue Date:	2010-12-09 13:54:34 (UTC+8)
Publisher:	國立中央大學
Abstract:	發展實用的駕駛輔助系統來確保駕駛安全已日漸成為一項重要的課題。駕駛者在高速公路上所面臨的主要危險來自於與前車未保持適當距離而導致可能的車輛碰撞。因此，得知與前車以及周圍車輛的相對位置將可大幅降低此種危險。在本論文中，我們發展一個高速公路前車偵測/追蹤機制，設計以單眼視覺為基礎之系統用來偵測前方車輛位置。我們的方法主要根據一種型體之方法論，即隱含式外型模型。我們先由實際場景得到欲用來訓練的圖像，在藉此建立一個碼簿。這些收集到的訓練圖像可被區分成三個部份：完整拍攝車後的景象，部分從左方拍攝車後的景象，及部分從右方拍攝車後的景象。透過使用尺度不變特徵轉換 (SIFT) 擷取興趣點，我們可以擁有代表前方車輛的良好特徵。接著，我們利用群聚的方式集群這些特徵來建立我們的碼簿。為了偵測以及追蹤物件，我們再次使用尺度不變特徵轉換在實際場景上。在每一個場景中，我們比較擷取出來的特徵與碼簿以找出匹配的代表特徵。一旦模型建立完成，我們可以根據尺度以及在模型內指示出的位置辨識出有興趣區間 (ROI) 。我們可以繼續搜尋先前定義的左邊車輛ROI以及右邊車輛ROI以偵測更多可能的車輛。實驗結果顯示車輛可以在三個區域中被偵測。Developing a practical driver assistance system for ensuring driving safety has become an increasingly important issue. The major risk of driving on the highway comes from possible collisions of the vehicle with the preceding one because a suitable distance is not well maintained. Therefore, knowing the relative position of the preceding vehicle and the surrounding cars should significantly reduce the risks. In this thesis, we would like to develop a highway preceding vehicle detection/tracking scheme, in which a monocular vision-based system for detecting the preceding vehicle in close and mid-range view will be designed to help provide a better view for the drivers. Our approach is based on an appearance-based methodology, i.e. Implicit Shape Model. A codebook is built for vehicle detection and tracking by using the training images captured from the real scenes. The collection of training images are divided into three parts: fully rear view, partially rear view from left and from the right sides. By applying scale-invariant feature transform (SIFT) to extract the interest points, we have a set of good features presenting the preceding vehicles. Then, we group those features to build up the codebook by clustering. Three models will thus be constructed. For detection and tracking the objects, we apply SIFT detector again in the real scenes. In each scene, we compare the extracted features with the codebook to find its matched representative features. Once a model is found, we can identify the ROI based on the scale and position indicated in the models. We can continue searching for the left and right side of pre-identified ROI to detect more possible vehicles. The experimental results show that vehicles can be detected in each of the three areas, i.e. right in front of the diver and his left/right-hand side areas.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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