Recursive Video Lane Detection

Dongkwon Jin; Dahyun Kim; Chang Su Kim

doi:10.1109/ICCV51070.2023.00778

Recursive Video Lane Detection

Dongkwon Jin^*
, Dahyun Kim
, Chang Su Kim

^*Corresponding author for this work

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

9 Citations (Scopus)

Abstract

A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame. RVLD consists of an intra-frame lane detector (ILD) and a predictive lane detector (PLD). First, we design ILD to localize lanes in a still frame. Second, we develop PLD to exploit the information of the previous frame for lane detection in a current frame. To this end, we estimate a motion field and warp the previous output to the current frame. Using the warped information, we refine the feature map of the current frame to detect lanes more reliably. Experimental results show that RVLD outperforms existing detectors on video lane datasets. Our codes are available at https://github.com/dongkwonjin/RVLD.

Original language	English
Title of host publication	Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8439-8448
Number of pages	10
ISBN (Electronic)	9798350307184
DOIs	https://doi.org/10.1109/ICCV51070.2023.00778
Publication status	Published - 2023
Event	2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France Duration: 2023 Oct 2 → 2023 Oct 6

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
ISSN (Print)	1550-5499

Conference

Conference	2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Country/Territory	France
City	Paris
Period	23/10/2 → 23/10/6

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCV51070.2023.00778

Cite this

Recursive Video Lane Detection. / Jin, Dongkwon; Kim, Dahyun; Kim, Chang Su.
Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 8439-8448 (Proceedings of the IEEE International Conference on Computer Vision).

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

Jin, D, Kim, D & Kim, CS 2023, Recursive Video Lane Detection. in Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023. Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., pp. 8439-8448, 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, 23/10/2. https://doi.org/10.1109/ICCV51070.2023.00778

@inproceedings{0bf15b15e4e24cbaa1e50532348c394f,

title = "Recursive Video Lane Detection",

abstract = "A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame. RVLD consists of an intra-frame lane detector (ILD) and a predictive lane detector (PLD). First, we design ILD to localize lanes in a still frame. Second, we develop PLD to exploit the information of the previous frame for lane detection in a current frame. To this end, we estimate a motion field and warp the previous output to the current frame. Using the warped information, we refine the feature map of the current frame to detect lanes more reliably. Experimental results show that RVLD outperforms existing detectors on video lane datasets. Our codes are available at https://github.com/dongkwonjin/RVLD.",

author = "Dongkwon Jin and Dahyun Kim and Kim, \{Chang Su\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 ; Conference date: 02-10-2023 Through 06-10-2023",

year = "2023",

doi = "10.1109/ICCV51070.2023.00778",

language = "English",

series = "Proceedings of the IEEE International Conference on Computer Vision",

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booktitle = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023",

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TY - GEN

T1 - Recursive Video Lane Detection

AU - Jin, Dongkwon

AU - Kim, Dahyun

AU - Kim, Chang Su

PY - 2023

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N2 - A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame. RVLD consists of an intra-frame lane detector (ILD) and a predictive lane detector (PLD). First, we design ILD to localize lanes in a still frame. Second, we develop PLD to exploit the information of the previous frame for lane detection in a current frame. To this end, we estimate a motion field and warp the previous output to the current frame. Using the warped information, we refine the feature map of the current frame to detect lanes more reliably. Experimental results show that RVLD outperforms existing detectors on video lane datasets. Our codes are available at https://github.com/dongkwonjin/RVLD.

AB - A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame. RVLD consists of an intra-frame lane detector (ILD) and a predictive lane detector (PLD). First, we design ILD to localize lanes in a still frame. Second, we develop PLD to exploit the information of the previous frame for lane detection in a current frame. To this end, we estimate a motion field and warp the previous output to the current frame. Using the warped information, we refine the feature map of the current frame to detect lanes more reliably. Experimental results show that RVLD outperforms existing detectors on video lane datasets. Our codes are available at https://github.com/dongkwonjin/RVLD.

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M3 - Conference contribution

AN - SCOPUS:85185875506

T3 - Proceedings of the IEEE International Conference on Computer Vision

SP - 8439

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BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023

Y2 - 2 October 2023 through 6 October 2023

ER -

Recursive Video Lane Detection

Abstract

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Bibliographical note

ASJC Scopus subject areas

Access to Document

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