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)

Abstract

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
PublisherSpringer
Pages448-458
ISBN (Print)978-3-319-16631-5
DOIs
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision (ACCV 2014) - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014
Conference number: 12
http://www.accv2014.org/

Publication series

NameLecture Notes in Computer Science
Volume9009
ISSN (Print)0302-9743

Conference

Conference12th Asian Conference on Computer Vision (ACCV 2014)
Abbreviated titleACCV 2014
CountrySingapore
CitySingapore
Period1/11/145/11/14
OtherACCV Workshop "My car has eyes: Intelligent vehicle with vision technology"
Internet address

Fingerprint

Advanced driver assistance systems
Support vector machines
Global positioning system
Navigation
Pixels
Experiments

Cite this

Li, C., Creusen, I. M., Hazelhoff, L., & With, de, P. H. N. (2015). Detection and recognition of road markings in panoramic images. In C. V. Jawahar, & S. Shan (Eds.), Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II (pp. 448-458). (Lecture Notes in Computer Science; Vol. 9009). Berlin: Springer. https://doi.org/10.1007/978-3-319-16631-5_33
Li, C. ; Creusen, I.M. ; Hazelhoff, L. ; With, de, P.H.N. / Detection and recognition of road markings in panoramic images. Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II. editor / C.V. Jawahar ; S. Shan. Berlin : Springer, 2015. pp. 448-458 (Lecture Notes in Computer Science).
@inproceedings{135a2021bfca45809f1029d37a61b09a,
title = "Detection and recognition of road markings in panoramic images",
abstract = "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.",
author = "C. Li and I.M. Creusen and L. Hazelhoff and {With, de}, P.H.N.",
year = "2015",
doi = "10.1007/978-3-319-16631-5_33",
language = "English",
isbn = "978-3-319-16631-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "448--458",
editor = "C.V. Jawahar and S. Shan",
booktitle = "Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II",
address = "Germany",

}

Li, C, Creusen, IM, Hazelhoff, L & With, de, PHN 2015, Detection and recognition of road markings in panoramic images. in CV Jawahar & S Shan (eds), Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II. Lecture Notes in Computer Science, vol. 9009, Springer, Berlin, pp. 448-458, 12th Asian Conference on Computer Vision (ACCV 2014), Singapore, Singapore, 1/11/14. https://doi.org/10.1007/978-3-319-16631-5_33

Detection and recognition of road markings in panoramic images. / Li, C.; Creusen, I.M.; Hazelhoff, L.; With, de, P.H.N.

Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II. ed. / C.V. Jawahar; S. Shan. Berlin : Springer, 2015. p. 448-458 (Lecture Notes in Computer Science; Vol. 9009).

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

TY - GEN

T1 - Detection and recognition of road markings in panoramic images

AU - Li, C.

AU - Creusen, I.M.

AU - Hazelhoff, L.

AU - With, de, P.H.N.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-16631-5_33

DO - 10.1007/978-3-319-16631-5_33

M3 - Conference contribution

SN - 978-3-319-16631-5

T3 - Lecture Notes in Computer Science

SP - 448

EP - 458

BT - Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II

A2 - Jawahar, C.V.

A2 - Shan, S.

PB - Springer

CY - Berlin

ER -

Li C, Creusen IM, Hazelhoff L, With, de PHN. Detection and recognition of road markings in panoramic images. In Jawahar CV, Shan S, editors, Computer Vision - ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part II. Berlin: Springer. 2015. p. 448-458. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-16631-5_33