Detection and recognition of road markings in panoramic images

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaties (Scopus)
419 Downloads (Pure)

Uittreksel

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.
Originele taal-2Engels
TitelVideo Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015
UitgeverijSPIE
Aantal pagina's10
DOI's
StatusGepubliceerd - 2015
Evenementconference; Electronic Imagining; 2015-02-08; 2015-02-12 -
Duur: 8 feb 201512 feb 2015

Publicatie series

NaamProceedings of SPIE
Volume9407

Congres

Congresconference; Electronic Imagining; 2015-02-08; 2015-02-12
Periode8/02/1512/02/15
AnderElectronic Imagining

Vingerafdruk

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

Citeer dit

Li, C., Creusen, I. M., Hazelhoff, L., & With, de, P. H. N. (2015). Detection and recognition of road markings in panoramic images. In Video Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015 [9407] (Proceedings of SPIE; Vol. 9407). SPIE. https://doi.org/10.1117/12.2081395
Li, C. ; Creusen, I.M. ; Hazelhoff, L. ; With, de, P.H.N. / Detection and recognition of road markings in panoramic images. Video Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015. SPIE, 2015. (Proceedings of SPIE).
@inproceedings{fc71cd561029474dac190c4028295d6c,
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.",
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Li, C, Creusen, IM, Hazelhoff, L & With, de, PHN 2015, Detection and recognition of road markings in panoramic images. in Video Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015., 9407, Proceedings of SPIE, vol. 9407, SPIE, conference; Electronic Imagining; 2015-02-08; 2015-02-12, 8/02/15. https://doi.org/10.1117/12.2081395

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

Video Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015. SPIE, 2015. 9407 (Proceedings of SPIE; Vol. 9407).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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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.

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Li C, Creusen IM, Hazelhoff L, With, de PHN. Detection and recognition of road markings in panoramic images. In Video Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015. SPIE. 2015. 9407. (Proceedings of SPIE). https://doi.org/10.1117/12.2081395