Lane detection in unstructured environments for autonomous navigation systems

Manh Cuong Le; Son Lam Phung; Abdesselam Bouzerdoum

doi:10.1007/978-3-319-16865-4_27

Lane detection in unstructured environments for autonomous navigation systems

Manh Cuong Le^*, Son Lam Phung, Abdesselam Bouzerdoum

^*Corresponding author for this work

University of Wollongong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

Automatic lane detection is an essential component for autonomous navigation systems. It is a challenging task in unstructured environments where lanes vary significantly in appearance and are not indicated by painted markers. This paper proposes a new method to detect pedestrian lanes that have no painted markers in indoor and outdoor scenes, under different illumination conditions. Our method detects the walking lane using appearance and shape information. To cope with variations in lane surfaces, an appearance model of the lane region is learned on-the-fly. A sample region for learning the appearance model is automatically selected in the input image using the vanishing point. This paper also proposes an improved method for vanishing point estimation, which employs local dominant orientations of edge pixels. The proposed method is evaluated on a new data set of 1600 images collected from various indoor and outdoor scenes that contain unmarked pedestrian lanes with different types and surface patterns. Experimental results and comparisons with other existing methods on the new data set have demonstrated the efficiency and robustness of the proposed method.

Original language	English
Title of host publication	Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
Editors	Ian Reid, Ming-Hsuan Yang, Hideo Saito, Daniel Cremers
Publisher	Springer Verlag
Pages	414-429
Number of pages	16
ISBN (Electronic)	9783319168647
DOIs	https://doi.org/10.1007/978-3-319-16865-4_27
Publication status	Published - 2015
Externally published	Yes
Event	12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore Duration: 1 Nov 2014 → 5 Nov 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9003
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th Asian Conference on Computer Vision, ACCV 2014
Country/Territory	Singapore
City	Singapore
Period	1/11/14 → 5/11/14

Access to Document

10.1007/978-3-319-16865-4_27

Cite this

Le, M. C., Phung, S. L., & Bouzerdoum, A. (2015). Lane detection in unstructured environments for autonomous navigation systems. In I. Reid, M.-H. Yang, H. Saito, & D. Cremers (Eds.), Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers (pp. 414-429). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9003). Springer Verlag. https://doi.org/10.1007/978-3-319-16865-4_27

Le, Manh Cuong ; Phung, Son Lam ; Bouzerdoum, Abdesselam. / Lane detection in unstructured environments for autonomous navigation systems. Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers. editor / Ian Reid ; Ming-Hsuan Yang ; Hideo Saito ; Daniel Cremers. Springer Verlag, 2015. pp. 414-429 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Lane detection in unstructured environments for autonomous navigation systems",

abstract = "Automatic lane detection is an essential component for autonomous navigation systems. It is a challenging task in unstructured environments where lanes vary significantly in appearance and are not indicated by painted markers. This paper proposes a new method to detect pedestrian lanes that have no painted markers in indoor and outdoor scenes, under different illumination conditions. Our method detects the walking lane using appearance and shape information. To cope with variations in lane surfaces, an appearance model of the lane region is learned on-the-fly. A sample region for learning the appearance model is automatically selected in the input image using the vanishing point. This paper also proposes an improved method for vanishing point estimation, which employs local dominant orientations of edge pixels. The proposed method is evaluated on a new data set of 1600 images collected from various indoor and outdoor scenes that contain unmarked pedestrian lanes with different types and surface patterns. Experimental results and comparisons with other existing methods on the new data set have demonstrated the efficiency and robustness of the proposed method.",

author = "Le, {Manh Cuong} and Phung, {Son Lam} and Abdesselam Bouzerdoum",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference date: 01-11-2014 Through 05-11-2014",

year = "2015",

doi = "10.1007/978-3-319-16865-4_27",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Le, MC, Phung, SL & Bouzerdoum, A 2015, Lane detection in unstructured environments for autonomous navigation systems. in I Reid, M-H Yang, H Saito & D Cremers (eds), Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9003, Springer Verlag, pp. 414-429, 12th Asian Conference on Computer Vision, ACCV 2014, Singapore, Singapore, 1/11/14. https://doi.org/10.1007/978-3-319-16865-4_27

Lane detection in unstructured environments for autonomous navigation systems. / Le, Manh Cuong; Phung, Son Lam; Bouzerdoum, Abdesselam.
Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers. ed. / Ian Reid; Ming-Hsuan Yang; Hideo Saito; Daniel Cremers. Springer Verlag, 2015. p. 414-429 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9003).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Lane detection in unstructured environments for autonomous navigation systems

AU - Le, Manh Cuong

AU - Phung, Son Lam

AU - Bouzerdoum, Abdesselam

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Automatic lane detection is an essential component for autonomous navigation systems. It is a challenging task in unstructured environments where lanes vary significantly in appearance and are not indicated by painted markers. This paper proposes a new method to detect pedestrian lanes that have no painted markers in indoor and outdoor scenes, under different illumination conditions. Our method detects the walking lane using appearance and shape information. To cope with variations in lane surfaces, an appearance model of the lane region is learned on-the-fly. A sample region for learning the appearance model is automatically selected in the input image using the vanishing point. This paper also proposes an improved method for vanishing point estimation, which employs local dominant orientations of edge pixels. The proposed method is evaluated on a new data set of 1600 images collected from various indoor and outdoor scenes that contain unmarked pedestrian lanes with different types and surface patterns. Experimental results and comparisons with other existing methods on the new data set have demonstrated the efficiency and robustness of the proposed method.

AB - Automatic lane detection is an essential component for autonomous navigation systems. It is a challenging task in unstructured environments where lanes vary significantly in appearance and are not indicated by painted markers. This paper proposes a new method to detect pedestrian lanes that have no painted markers in indoor and outdoor scenes, under different illumination conditions. Our method detects the walking lane using appearance and shape information. To cope with variations in lane surfaces, an appearance model of the lane region is learned on-the-fly. A sample region for learning the appearance model is automatically selected in the input image using the vanishing point. This paper also proposes an improved method for vanishing point estimation, which employs local dominant orientations of edge pixels. The proposed method is evaluated on a new data set of 1600 images collected from various indoor and outdoor scenes that contain unmarked pedestrian lanes with different types and surface patterns. Experimental results and comparisons with other existing methods on the new data set have demonstrated the efficiency and robustness of the proposed method.

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AN - SCOPUS:84938836825

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers

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ER -

Le MC, Phung SL, Bouzerdoum A. Lane detection in unstructured environments for autonomous navigation systems. In Reid I, Yang MH, Saito H, Cremers D, editors, Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers. Springer Verlag. 2015. p. 414-429. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16865-4_27

Lane detection in unstructured environments for autonomous navigation systems

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