Authors:
Jack Greenhalgh
and
Majid Mirmehdi
Affiliation:
University of Bristol, United Kingdom
Keyword(s):
Computer Vision, Machine Learning, Text Recognition, Intelligent Transportation Systems.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Understanding
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
;
Video Analysis
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.