The University of Southampton
University of Southampton Institutional Repository

Vehicle Detection and Recognition for Autonomous Intelligent Cruise Control

Vehicle Detection and Recognition for Autonomous Intelligent Cruise Control
Vehicle Detection and Recognition for Autonomous Intelligent Cruise Control
The introduction of new technologies such as autonomous intelligent cruise control or collision avoidance schemes to road vehicles necessitates a high degree of robustness and reliability. Whilst very accurate range estimates may be recovered using conventional sensors, e.g. millimetric radar, these typically suffer from both low bearing resolution and potential ambiguities through, for example, false alarms. This work details a novel two-stage vehicle detection and recognition algorithm which combines an image processing area of interest (AOI) designator to cue a secondary recognition process implemented using principal component analysis (PCA) as input to a Multi-Layered Perceptron (MLP) classifier. The combination of an initial detection phase, followed by a recognition process has allowed the classifier design to be greatly simplified. In turn the classifier performance has allowed some of the image processing assumptions to be relaxed, whilst maintaining a high signal to noise ratio (SNR). Both the image processing system and MLP classifier have been designed for real-time implementation and data-fusion with other information sources such as a range/range rate radar.
Matthews, N.D.
67f2b57f-7b49-47ef-8910-a7bada8af702
An, P.E.
5dc94657-d009-4d13-9a0f-6645a9d296d9
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Matthews, N.D.
67f2b57f-7b49-47ef-8910-a7bada8af702
An, P.E.
5dc94657-d009-4d13-9a0f-6645a9d296d9
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Matthews, N.D., An, P.E. and Harris, C.J. (1995) Vehicle Detection and Recognition for Autonomous Intelligent Cruise Control

Record type: Monograph (Project Report)

Abstract

The introduction of new technologies such as autonomous intelligent cruise control or collision avoidance schemes to road vehicles necessitates a high degree of robustness and reliability. Whilst very accurate range estimates may be recovered using conventional sensors, e.g. millimetric radar, these typically suffer from both low bearing resolution and potential ambiguities through, for example, false alarms. This work details a novel two-stage vehicle detection and recognition algorithm which combines an image processing area of interest (AOI) designator to cue a secondary recognition process implemented using principal component analysis (PCA) as input to a Multi-Layered Perceptron (MLP) classifier. The combination of an initial detection phase, followed by a recognition process has allowed the classifier design to be greatly simplified. In turn the classifier performance has allowed some of the image processing assumptions to be relaxed, whilst maintaining a high signal to noise ratio (SNR). Both the image processing system and MLP classifier have been designed for real-time implementation and data-fusion with other information sources such as a range/range rate radar.

This record has no associated files available for download.

More information

Published date: 1995
Additional Information: 1995/6 Research Journal Address: Department of Electronics and Computer Science
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250100
URI: http://eprints.soton.ac.uk/id/eprint/250100
PURE UUID: d9a90fff-9c5c-4c56-80ea-2cc3b72104aa

Catalogue record

Date deposited: 04 May 1999
Last modified: 20 Feb 2024 18:05

Export record

Contributors

Author: N.D. Matthews
Author: P.E. An
Author: C.J. Harris

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×