Detection approach of vision based on road vehicle

MIAO Xiao-dong; LI Shun-ming; SHEN Huan; LIU Jian-ya; AN Mu-jin

doi:10.13873/j.1000-97872010.02.016

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Detection approach of vision based on road vehicle

更新时间：2025-09-11

- Detection approach of vision based on road vehicle
- Vol. 29, Issue 2, Pages: 5-8+12(2010)
- 作者机构：
  
  南京航空航天大学能源与动力学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872010.02.016
  CLC： TP391.41
- Published：2010
- 稿件说明：
移动端阅览
[1]缪小冬,李舜酩,沈峘,刘建娅,安木金.路面车辆的视觉检测方法[J].传感器与微系统,2010,29(02):5-8+12.

MIAO Xiao-dong, LI Shun-ming, SHEN Huan, et al. Detection approach of vision based on road vehicle[J]. 2010, 29(2): 5-8+12.
[1]缪小冬,李舜酩,沈峘,刘建娅,安木金.路面车辆的视觉检测方法[J].传感器与微系统,2010,29(02):5-8+12. DOI： 10.13873/j.1000-97872010.02.016.

MIAO Xiao-dong, LI Shun-ming, SHEN Huan, et al. Detection approach of vision based on road vehicle[J]. 2010, 29(2): 5-8+12. DOI： 10.13873/j.1000-97872010.02.016.

摘要

智能车辆是近年来的研究热点

基于视觉的车辆检测与识别技术是智能车辆技术研究的一个重要组成部分。介绍国内外路面车辆检测技术的研究方法和发展现状。根据检测的不同方法

以视觉传感器为主

从特征和运动的角度论述目标检测的方法;从模板、外观和分类的角度论述目标识别的方法。介绍立体视觉和多传感器融合的技术。对智能车辆障碍物检测和识别技术的发展进行了展望。

Abstract

Intelligent vehicle is an active research field recently

the vision-based vehicle detection and recognition is an important aspect in the intelligent vehicle field.Detection and recognition on road in different countries are introduced.From features and movement point of view

target detection method are discussed.From template

appearance and classification point of view

target identifieation are discussed.The technology of stereo vision and multi-sensor fusion is introduced also.The delelopment of the technology of intelligent vehicle obstacles detection is expected.

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references

智能车辆发展及其关键技术研究现状 [J]. 李舜酩,沈峘,毛建国,辛江慧,缪小东. 传感器与微系统 . 2009(01)

单目视觉车道线识别算法及其ARM实现 [J]. 沈峘,毛建国,李舜酩. 南京航空航天大学学报 . 2008(02)

智能车辆的障碍物检测研究方法综述 [J]. 王荣本,赵一兵,李琳辉,张明恒. 公路交通科技 . 2007(11)

Color machine vision for autonomous vehicles [J] . Shashi D. Buluswar,Bruce A. Draper. Engineering Applications of Artificial Intelligence . 1998 (2)

A real-time oriented system for vehicle detection [J] . Massimo Bertozzi,Alberto Broggi,Stefano Castelluccio. Journal of Systems Architecture . 1997 (1)

Vehicle detection and recognition in greyscale imagery [J] . N.D. Matthews,P.E. An,D. Charnley,C.J. Harris. Control Engineering Practice . 1996 (4)

Artificial neural networks in real-time car detection and tracking applications [J] . Christian Goerick,Detlev Noll,Martin Werner. Pattern Recognition Letters . 1996 (4)

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