Vehicle detection is one of the key technologies in ITS.
车辆检测传感器是ITS中最重要的交通数据采集设备之一。
A vehicle detection method using monocular vision is proposed.
提出了一种应用单目视觉进行车辆检测的方法。
It provides a new way for space vehicle detection and positioning.
为空间飞行器的探测提供了一种新型的手段。
Feature extraction of the local vehicle is the key of vehicle detection.
车辆局部特征提取是车辆检测的关键。
In this section, we present an improved video-based vehicle detection process.
在这一部分，本文提出了一种改进的基于视频的车辆检测流程。
Then we compared several of video based vehicle detection algorithms in detail.
在此基础上，对各种基于视频的车辆检测算法分别进行分析比较。
This paper describes vehicle detection and tracking method based on vehicle feature.
本文提出了一种基于车辆特征的方法识别和跟踪前方的车辆。
The background subtraction is an important approach in video-based vehicle detection.
背景差法是一种重要的视频车辆检测方法。
Vehicle detection is very important in a good automatic transport incident detection system.
一个好的自动交通事件检测系统，车辆检测是关键。
Presents a preceding vehicle detection algorithm based on multiple scale edge and local entropy.
提出一种基于多尺度边缘和局部熵原理的前方车辆的检测算法。
In order to get the moving vehicle detection, vehicle occlusion detection is as an essential link.
为了更好进行运动车辆检测，遮挡检测成为一个必不可少的环节。
It is important to extract RIO(region of interest) in vehicle detection system based on video image.
在基于视频图像方法的车辆检测系统中，兴趣区域的提取是至关重要的。
This paper proposes a fast and effective method for moving vehicle detection under cloudy conditions.
针对多云天气条件下的车辆检测问题，提出了一种快速有效的车辆检测方法。
The issue of vehicle detection is the basic and key problem in Intelligent Traffic Systems technology.
车辆检测是智能交通系统的基础核心问题。
The main contents of this paper are organized as follows: 1, Vehicle detection based on video sequence.
本文主要包括的研究工作如下：1、基于视频序列的车辆检测。
The moving vehicle detection and tracking are important part of modern Intelligent Transportation System.
运动车辆的检测与跟踪是现代智能交通系统的重要组成部分。
Research on the technology for the vehicle detection based on image processing is just the hot of the field.
利用数字图像处理技术来实现交通流量的车辆检测技术已成为该研究领域的热点。
A leading vehicle detection and recognition method based on machine vision is mainly described in this paper.
着重阐述基于机器视觉的前方车辆障碍物检测方法。
Absrtact: in the field of intelligent transportation, violation vehicle detection was an important technique.
摘要：在智能交通领域，车辆的违章检测是重要的技术。
Then we introduced several of video based vehicle detection algorithms in detail and compared their performance.
在此基础上，对各种基于视频的车辆检测算法分别进行了详细介绍，并给出了它们的性能比较。
Vehicle detection method based video and traffic flow detection system based fictitious loop are studied in details.
本文详细地研究了车辆目标的视频检测方法和基于虚拟线圈的交通流量检测系统。
It is an intelligent system that include many techniques, such as video vehicle detection, license plate recognition.
它是集运动车辆视频检测、车牌识别等技术于一体的智能化车辆检测系统。
Shadows result from roadside building or trees are one of the factors that arise errors in video based vehicle detection.
但是由路边建筑、树木引起的阴影，是导致车辆检测错误的一大主要因素。
There are usually three approaches for vehicle detection, that is frame differencing, background differencing and edge detection.
车辆检测常用的有帧差法、背景差法和边缘检测法等。
Video vehicle detection system is a computer processing system which uses image processing technology to detect the traffic flow.
视频车辆检测系统是一种利用图像处理和模式识别技术实现对交通目标检测和识别的计算机处理系统。
Intelligent transportation system is based on realizing moving vehicle detection and collection with traffic message in real time.
智能运输系统的前提条件之一是实现道路行驶车辆自动检测与交通信息实时采集。
Motion vehicle detection is the precondition of vehicle tracking, it is very important but a difficult problem of vehicle motion analysis.
运动车辆的正确检测是车辆跟踪的前提，是车辆运动分析的一个重点和难点。
The research content of this paper is vehicle detection and tracking based on a single camera. It is mainly to detect and track front vehicles.
本文的研究内容是基于单个摄像机的车辆检测与跟踪，主要是对本车前方的运动车辆进行检测和跟踪。
The data sets which consist of training images and testing images are collected, and video data set is collected for real-time vehicle detection.
本文建立了针对车辆前后视图的训练样本库和测试图像库，并且建立了用于实时车辆识别的视频库。
The data sets which consist of training images and testing images are collected, and video data set is collected for real-time vehicle detection.
本文建立了针对车辆前后视图的训练样本库和测试图像库，并且建立了用于实时车辆识别的视频库。
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