Detection and Recognition of Painted Road Surface Markings

Jack Greenhalgh, Majid Mirmehdi

2015

Abstract

A method for the automatic detection and recognition of text and symbols painted on the road surface is presented. Candidate regions are detected as maximally stable extremal regions (MSER) in a frame which has been transformed into an inverse perspective mapping (IPM) image, showing the road surface with the effects of perspective distortion removed. Detected candidates are then sorted into words and symbols, before they are interpreted using separate recognition stages. Symbol-based road markings are recognised using histogram of oriented gradient (HOG) features and support vector machines (SVM). Text-based road signs are recognised using a third-party optical character recognition (OCR) package, after application of a perspective correction stage. Matching of regions between frames, and temporal fusion of results is used to improve performance. The proposed method is validated using a data-set of videos, and achieves F-measures of 0.85 for text characters and 0.91 for symbols.

Paper Citation

in Harvard Style

Greenhalgh J. and Mirmehdi M. (2015). Detection and Recognition of Painted Road Surface Markings . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 130-138. DOI: 10.5220/0005273501300138

in Bibtex Style

@conference{icpram15,
author={Jack Greenhalgh and Majid Mirmehdi},
title={Detection and Recognition of Painted Road Surface Markings},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={130-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005273501300138},
isbn={978-989-758-076-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Detection and Recognition of Painted Road Surface Markings
SN - 978-989-758-076-5
AU - Greenhalgh J.
AU - Mirmehdi M.
PY - 2015
SP - 130
EP - 138
DO - 10.5220/0005273501300138