A practical system for road marking detection and recognition

Author

Tao Wu ; Ranganathan, A.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA

fYear

2012

fDate

3-7 June 2012

Firstpage

25

Lastpage

30

Abstract

We present a system for detecting and recognizing road markings from video input obtained from an in-car camera. Our system learns feature-based templates of road markings from training data and matches these templates to detected features in the test images during runtime. We use MSER features and perform the template matching in an efficient manner so that our system can detect multiple road markings in a single image. Our system also scales well with the number of categories of road markings to be detected. For evaluating our system, we present an extensive dataset (available from www.ananth.in/RoadMarkingDetection.html) of road markings with ground truth labels, which we hope will be useful as a benchmark dataset for future researchers in this area.

Keywords

automobiles; cameras; feature extraction; image matching; learning (artificial intelligence); object detection; object recognition; traffic engineering computing; video signal processing; MSER features; feature detection; feature-based template learning; ground truth labels; in-car camera; practical system; road marking detection; road marking recognition; template matching; test images; training data; video input; Cameras; Feature extraction; Image edge detection; Roads; Shape; Vectors; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Vehicles Symposium (IV), 2012 IEEE

Conference_Location

Alcala de Henares

ISSN

1931-0587

Print_ISBN

978-1-4673-2119-8

Type

conf

DOI

10.1109/IVS.2012.6232144

Filename

6232144