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   vehicle detection 的翻译结果: 查询用时:0.177秒
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vehicle detection
相关语句
  车辆检测
     Video vehicle detection and segmentation based on Mean Shift method
     基于Mean Shift方法的视频车辆检测与分割
短句来源
     Infrared Vehicle Detection with Gabor Filter and SVM Classifier
     基于Gabor滤波器和SVM分类器的红外车辆检测
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     Online vehicle detection algorithm based on PLA time series representation
     基于PLA时间序列重现的在线车辆检测算法
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     WSN Based Road Vehicle Detection and Fusion Study Using Video and Magnetic Sensor
     基于WSN的视频与磁敏传感器道路车辆检测及其融合研究
短句来源
     1. Image acquisitionAimed at practical problems in the design of vehicle detection and image sampling, we select effective measures to the system after a lot of compares in selection aspect of acquisition detection facility.
     1.图像采集部分在车辆检测模块与图像采集模块的设计中针对实际遇到的问题,在采集检测设备的选取方面做了大量的比较和尝试,最终选取了一套行之有效的措施应用于本系统的设计中。
短句来源
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  车辆探测
     On-Road Vehicle Detection Based on Gabor Filters
     基于Gabor滤波器的前方车辆探测
短句来源
     Study on Method of Multiple Vehicle Detection and Tracking Based on Vision
     基于机器视觉的道路上前方多车辆探测方法研究
     Application of AMR sensors to vehicle detection
     各向异性磁阻传感器在车辆探测中的应用
短句来源
     A Review of the Researches oo Vehicle Detection in Advanced Driver Assistance System
     先进驾驶员辅助系统中的车辆探测研究综述
短句来源
     A Study on Multiple Vehicle Detection Based on Computer Vision
     基于机器视觉的道路上前方多车辆探测方法研究
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  “vehicle detection”译为未确定词的双语例句
     Four methods of vehicle detection and tracking were compared and analyzed according to the sensors used including single camera,stereo vision,sensor fusion and novision sensor.
     根据探测车辆所采用的传感器不同,分别对采用单目视觉、双目视觉、视觉与其他传感器融合以及非视觉传感器的几种探测方法进行了比较与分析。
短句来源
     Algorithm of Vehicle Detection and Pattern Recognition Using SVM
     基于SVM的车型检测和识别算法
短句来源
     Algorithm of Vehicle Detection by Video Image Based on Wavelet Decomposition
     基于小波分解的车辆视频检测算法
短句来源
     This thesis researches video vehicle detection and license plate recognition, which are the two key techniques of MVDS.
     本文详细研究了移动查车系统的两项关键核心技术:运动车辆视频检测技术和基于视频图像的车牌识别技术。
短句来源
     Adaptive HSV Color Background Modeling for Real-time Vehicle Detection
     HSV空间自适应背景模板在运动车辆实时检测中的应用
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  vehicle detection
Vehicle detection and hypothesis generation are performed using template correlation and a 3D wire frame model of the vehicle is fitted to the image.
      
Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions.
      
This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile-mounted camera.
      
Real-time multiple vehicle detection and tracking from a moving vehicle
      
We will conduct tests on freeways for vehicle detection, classification, speed estimation and re-identification.
      
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msed on a STD-bus industry control computer and a SSI personal compuler, a movable ve-hicle delecting line is made. It fully realizes the automation of the whole vehicle detection procedurefrom reghtration , detection' display and statistics to prnting result report form.

以STD工业控制机为中心,SSI个人计算机为后台子机,构成了流动式汽车检测系统,实现了登记、检测、显示、统计、打印报表等工作的汽车性能检测过程的自动化。

Automatic traffic monitoring plays an important role in the truly Intelligent Vehicle/Highway System(IVHS). Vision-based approach is promising since it requires no pavement adjustments and has more potential advantages such as larger detection areas and more flexible functions. However traffic flow raises interesting but difficult problems for image processing. The various light conditions, as the result of variety of weather, places a strong need on the robust algorithms, which require a great amount of computational...

Automatic traffic monitoring plays an important role in the truly Intelligent Vehicle/Highway System(IVHS). Vision-based approach is promising since it requires no pavement adjustments and has more potential advantages such as larger detection areas and more flexible functions. However traffic flow raises interesting but difficult problems for image processing. The various light conditions, as the result of variety of weather, places a strong need on the robust algorithms, which require a great amount of computational power to meet the real-time operations of the traffic monitoring system. Great research efforts have been put on this topic all over the world, but most of the current commercial traffic monitoring image systems are cost expensive.In this paper we present a novel approach using 2D spatio-temporal images. The TV camera is mounted above the highway. The traffic is monitored and analyzed through two slice windows for each lane the vehicle detection window is along the 2D spatio - temporal (ST) images: the panoramic view image (PVI) and the epipolar plane image (EPI). The primary problem, The separation and counting of vehicles and identifying their class(size) and speed,is solved through analyzing these two ZD ST images. The problem of camera settings, ST image calibration and rectification, data integration in vehicle detection, accurate speed estimation, background updating are discussed in the paper.The features and advantages of the proposed ST approach are: (1) Adaptive signal selection. Only the vital information which is enough for the given tasks is selected. (2) Computational efficiency. Only a few scan lines are processed in each frame, and ST images are more generic and simple than frame images in this special application. (3) Information completeness. Narrow spatial viewing windows are compensatedby dense temporal sequences, and the partially-viewed large vehicles in a single frame can be reconstructed by using ST images. (4)Accurate speed estimation. Speed is estimated from the loci of the front and rear instead of the locations at two single instants. (5) Easy background updating. Updating of the background estimation is very important in dealing with the changing weather conditions, which can be done easily according to the information from the few scan lines in the ST image method. (6) COmpact representation.PVI is a compressed and visually explicit representation for the traffic flow. So PVI can be saved on the hard disk and takes the place of the traditional video tapes. We have built up a prototype system using the methods presented in the paper.

本论文提出了一个利用二维时空图象进行交通自动监测的新方法。摄像机架设在公路上方,通过两个细缝检测窗口──垂直于道路方向的车辆检测窗和平行于道路方向的速度检测窗,便形成用于交通自动检测的二维全景图(PVI)和外极面图(EPI)。本文讨论时空图象生成和校准的方法,车辆检测的信息融合方法和速度估计的时空轨迹法。实验表明,基于低成本的硬件,可实时得到车辆计数、分类和速度计量等基本交通参数。

Moving vehicle detection through substracting background was presented. The background was updated based on Kalman filter theory.The detection was implemented with the processor TMS320C25.For a field of 256×256 the device can perform 3~4 times per second.

用动态图像序列分析减背景方法检测出了高速公路上的汽车目标,得到流量、速度等参数.初始背景用卡尔曼滤波更新,能自适应环境光的慢变.给出了目标检测跟踪算法及实现算法的TMS320C25脱机硬件系统结构.跟踪实际交通图像结果表明,算法性能良好,处理速度为3~4次/s,满足了交通管理的实时性要求.

 
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