Detection and recognition of road markings in panoramic images

C. Li, I.M. Creusen, L. Hazelhoff, P.H.N. With, de

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)


The detection of road markings has many practical applications, such as advanced driver assistance systems, road maintenance and accurate GPS navigation. In this paper we propose an algorithm to detect and recognize road markings from panoramic images. Our algorithm consists of four steps. First, an inverse perspective mapping is applied to the panoramic image, and the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, we extract the distance between the center and the boundary at regular angular steps of each considered potential road marking segment into a feature vector. Third, each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous false positive detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is capable of recognizing 93%, 95% and 91% of striped line segments, blocks and arrows respectively, as well as 94% of the lane markings.
Original languageEnglish
Title of host publicationComputer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II
EditorsC.V. Jawahar, S. Shan
Place of PublicationBerlin
ISBN (Print)978-3-319-16631-5
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision (ACCV 2014) - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014
Conference number: 12

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference12th Asian Conference on Computer Vision (ACCV 2014)
Abbreviated titleACCV 2014
OtherACCV Workshop "My car has eyes: Intelligent vehicle with vision technology"
Internet address


Dive into the research topics of 'Detection and recognition of road markings in panoramic images'. Together they form a unique fingerprint.

Cite this