1、毕 业 设 计（论 文）外 文 参 考 资 料 及 译 文译文题目： A new vehicle detection method 一种新的车辆检测方法 学生姓名： 学号： 专业： 所在学院： 指导教师： 职称： 20xx年 02 月 25 日说明：要求学生结合毕业设计（论文）课题参阅一篇以上的外文资料，并翻译至少一万印刷符（或译出3千汉字）以上的译文。译文原则上要求打印（如手写，一律用400字方格稿纸书写），连同学校提供的统一封面及英文原文装订，于毕业设计（论文）工作开始后2周内完成，作为成绩考核的一部分。英语原文A new vehicle detection methodZebbara K
2、halid Abdenbi Mazoul Mohamed El AnsariLabSIV, Department of Computer ScienceLabSIV, Department of Computer ScienceLabSIV, Department of Computer ScienceFaculty of Science, Ibn Zohr University Agadir, MoroccoFaculty of Science, University of Ibn Zohr Agadir, MoroccoFaculty of Science, University of I
3、bn Zohr Agadir, MoroccoAbstractThis paper presents a new vehicle detection method from images acquired by cameras embedded in a moving vehicle. Given the sequence of images, the proposed algorithms should detect out all cars in realtime. Related to the driving direction, the cars can be classified i
4、nto two types. Cars drive in the same direction as the intelligent vehicle (IV) and cars drive in the opposite direction. Due to the distinct features of these two types, we suggest to achieve this method in two main steps. The first one detects all obstacles from images using the so-called associat
5、ion combined with corner detector. The second step is applied to validate each vehicle using AdaBoost classifier. The new method has been applied to different images data and the experimental results validate the efficacy of our method.Keywords-component; intelligent vehicle; vehicle detection; Asso
6、ciation; Optical Flow; AdaBoost; Haar filter.I. INTRODUCTIONDetection of road obstacles 1 2 3 4 5 6 is an important task in the intelligent transportation. A number of sensors embedded in IV to perform the vehicle detection task. These sensors can be classified into passive and active sensors. Known
7、 that active sensors are expensive and cause pollution to the environment, we propose to use passive sensors in our vehicle detection approach. The data we are going to process to achieve vehicle detection are images taken from a camera embedded in a moving car.In the field of technical obstacle det
8、ected by vision system, two approaches existed: the first approach is unicameral approach that uses a single camera that consists of an image interpretation with former knowledge of information about these obstacles. This information can be texture information 7, color 8, 9. The second one is the st
9、ereo or multi-camera approach which is based on the variation map after matching primitives between different views of the sensor 10, 11 and 12. Vehicle detection algorithms have two basic step; Hypothesis Generation (HG) and Hypothesis Verification (HV) 13. In the hypothesis Generation step, the al
10、gorithm hypothesizes the locations of vehicles in an image. In the Hypothesis Verification (HV) step, he algorithm verifies the presence of vehicle in an image. The methods in the HG step can be categorized into tree methods; Knowledge-based methods which use symmetry of object, color, corners and e
11、dges; Stereo-vision-based methods which use two cameras;Motion-based Methods which track the motion of pixels between the consecutive frames 14. The methods in the HV step are Template-based methods and Appearance methods. Template-based methods use predefined patterns of the vehicle class. Appearan
12、ce-based methods include pattern classification system between vehicle and non vehicle. There are a many works 151617 tackling realtime on-road vehicle detection problem. All the papers used monocular cameras and have real- time constraints. 15 used horizontal and vertical edges (Knowledge-based met
13、hods) in HG step. The selected regions at HG step are matched with predefined template in HV step. 16 used horizontal and vertical edges in HG step. However, they use Haar Wavelet Transform and SVMs (Appearance- based methods) in HV step. 17 detected long-distance stationary obstacles including vehi
14、cles. They used an efficient optical flow algorithm 18 in HG step. They used Sum of squared differences (SSD) with a threshold value to verify their hypothesis.This paper presents a new approach for vehicle detection. At each time, the decision of the presence of vehicles in the road scene is made b
15、ased on the current frame and its preceding one. We use the association approach 20, which consists in finding the relationship between consecutive frames. This method exploits the displacement of edges in the frames. At each edge point in one frame we look for its associate one in the preceding frame if any. Obstacles can be detected on the basis of the analysis of association results. Adaboost classifier is used to verify is an obst
