A Preliminary Investigation into an Intelligent Car Headlight Dipping System

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current vision systems for driver assistance are now a huge focus within car manufacturing compaines. Such systems use sensor techniques to detect moving objects, such as pedestrians, or cameras where 360 degree vision is possible using front and rear cameras. The main advantage of using cameras is that visual data is crucial to the detection of moving objects within lanes, the recognition of traffic signs, pedestrians and of particular interest here, car headlights. However, a reliable vision based driver assistance system using even the most sophisticated vision system is extremely difficult as vehicles and objects vary in shape, size and colour and outdoor environments can be very complex. An essential feature of car vision systems is real time recognition of objects and environments. This paper presents a preliminary study into the development of a real-time automatic car headlight dipping using an appropriate combination of image analysis, neural networks topologies and training paradigms.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages156-163
Number of pages8
Publication statusPublished - 30 Aug 2006
EventIrish Machine Vision and Image Processing Conference - Dublin City University, Ireland
Duration: 30 Aug 2006 → …

Conference

ConferenceIrish Machine Vision and Image Processing Conference
Period30/08/06 → …

Fingerprint

Headlights
Railroad cars
Cameras
Traffic signs
Real time systems
Image analysis
Topology
Color
Neural networks
Sensors

Cite this

@inproceedings{b009111066e94b03a157a1acf5987d8e,
title = "A Preliminary Investigation into an Intelligent Car Headlight Dipping System",
abstract = "Current vision systems for driver assistance are now a huge focus within car manufacturing compaines. Such systems use sensor techniques to detect moving objects, such as pedestrians, or cameras where 360 degree vision is possible using front and rear cameras. The main advantage of using cameras is that visual data is crucial to the detection of moving objects within lanes, the recognition of traffic signs, pedestrians and of particular interest here, car headlights. However, a reliable vision based driver assistance system using even the most sophisticated vision system is extremely difficult as vehicles and objects vary in shape, size and colour and outdoor environments can be very complex. An essential feature of car vision systems is real time recognition of objects and environments. This paper presents a preliminary study into the development of a real-time automatic car headlight dipping using an appropriate combination of image analysis, neural networks topologies and training paradigms.",
author = "SA Coleman and Liam McDaid and B Gardiner",
year = "2006",
month = "8",
day = "30",
language = "English",
isbn = "0-9553885-0-3",
pages = "156--163",
booktitle = "Unknown Host Publication",

}

Coleman, SA, McDaid, L & Gardiner, B 2006, A Preliminary Investigation into an Intelligent Car Headlight Dipping System. in Unknown Host Publication. pp. 156-163, Irish Machine Vision and Image Processing Conference, 30/08/06.

A Preliminary Investigation into an Intelligent Car Headlight Dipping System. / Coleman, SA; McDaid, Liam; Gardiner, B.

Unknown Host Publication. 2006. p. 156-163.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Preliminary Investigation into an Intelligent Car Headlight Dipping System

AU - Coleman, SA

AU - McDaid, Liam

AU - Gardiner, B

PY - 2006/8/30

Y1 - 2006/8/30

N2 - Current vision systems for driver assistance are now a huge focus within car manufacturing compaines. Such systems use sensor techniques to detect moving objects, such as pedestrians, or cameras where 360 degree vision is possible using front and rear cameras. The main advantage of using cameras is that visual data is crucial to the detection of moving objects within lanes, the recognition of traffic signs, pedestrians and of particular interest here, car headlights. However, a reliable vision based driver assistance system using even the most sophisticated vision system is extremely difficult as vehicles and objects vary in shape, size and colour and outdoor environments can be very complex. An essential feature of car vision systems is real time recognition of objects and environments. This paper presents a preliminary study into the development of a real-time automatic car headlight dipping using an appropriate combination of image analysis, neural networks topologies and training paradigms.

AB - Current vision systems for driver assistance are now a huge focus within car manufacturing compaines. Such systems use sensor techniques to detect moving objects, such as pedestrians, or cameras where 360 degree vision is possible using front and rear cameras. The main advantage of using cameras is that visual data is crucial to the detection of moving objects within lanes, the recognition of traffic signs, pedestrians and of particular interest here, car headlights. However, a reliable vision based driver assistance system using even the most sophisticated vision system is extremely difficult as vehicles and objects vary in shape, size and colour and outdoor environments can be very complex. An essential feature of car vision systems is real time recognition of objects and environments. This paper presents a preliminary study into the development of a real-time automatic car headlight dipping using an appropriate combination of image analysis, neural networks topologies and training paradigms.

M3 - Conference contribution

SN - 0-9553885-0-3

SP - 156

EP - 163

BT - Unknown Host Publication

ER -