Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment

Li Wen Wang; Du Li; Wan Chi Siu; Daniel Pak Kong Lun

doi:10.1117/12.2626131

Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment

Li Wen Wang, Du Li, Wan Chi Siu, Daniel Pak Kong Lun

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

This paper gives focus on multi-lane detection from traffic cameras, which is based on automatic trajectory analysis and is promoted with advanced deep-learning technologies. Our proposed approach is based on big trajectory data that is robust to complex road scenes, which makes our approach particularly reliable and practical for Intelligent Transportation Systems. By using the deep learning object detection technology, it firstly generates big trajectory data on the road. Then, it detects the stop lines on the road and counts the number of lanes from the trajectories. Next, the trajectories are divided into different groups, where each group contains the trajectories of one lane. Finally, the lanes are fitted by the grouped trajectories. A large number of experiments have been done. The results show that the proposed approach can effectively detect the lanes on the road.

Original language	English
Title of host publication	International Workshop on Advanced Imaging Technology, IWAIT 2022
Editors	Masayuki Nakajima, Shogo Muramatsu, Jae-Gon Kim, Jing-Ming Guo, Qian Kemao
Publisher	SPIE
Volume	12177
ISBN (Electronic)	9781510653313
DOIs	https://doi.org/10.1117/12.2626131
Publication status	Published - 30 Apr 2022
Event	2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 - Hong Kong, China Duration: 4 Jan 2022 → 6 Jan 2022

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	12177
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	2022 International Workshop on Advanced Imaging Technology, IWAIT 2022
Country/Territory	China
City	Hong Kong
Period	4/01/22 → 6/01/22

Keywords

intelligent transportation system
lane detection
trajectory analysis

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

More information

10.1117/12.2626131

Cite this

Wang, L. W., Li, D., Siu, W. C., & Lun, D. P. K. (2022). Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment. In M. Nakajima, S. Muramatsu, J.-G. Kim, J.-M. Guo, & Q. Kemao (Eds.), International Workshop on Advanced Imaging Technology, IWAIT 2022 (Vol. 12177). Article 1217725 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12177). SPIE. https://doi.org/10.1117/12.2626131

Wang, Li Wen ; Li, Du ; Siu, Wan Chi et al. / Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment. International Workshop on Advanced Imaging Technology, IWAIT 2022. editor / Masayuki Nakajima ; Shogo Muramatsu ; Jae-Gon Kim ; Jing-Ming Guo ; Qian Kemao. Vol. 12177 SPIE, 2022. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{551d44ab83324084b381be9ebca4c207,

title = "Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment",

abstract = "This paper gives focus on multi-lane detection from traffic cameras, which is based on automatic trajectory analysis and is promoted with advanced deep-learning technologies. Our proposed approach is based on big trajectory data that is robust to complex road scenes, which makes our approach particularly reliable and practical for Intelligent Transportation Systems. By using the deep learning object detection technology, it firstly generates big trajectory data on the road. Then, it detects the stop lines on the road and counts the number of lanes from the trajectories. Next, the trajectories are divided into different groups, where each group contains the trajectories of one lane. Finally, the lanes are fitted by the grouped trajectories. A large number of experiments have been done. The results show that the proposed approach can effectively detect the lanes on the road.",

keywords = "intelligent transportation system, lane detection, trajectory analysis",

author = "Wang, {Li Wen} and Du Li and Siu, {Wan Chi} and Lun, {Daniel Pak Kong}",

note = "Funding Information: This work was supported by The Hong Kong Polytechnic University, Caritas Institute of Higher Education (ISG200206) and Research Grants Council of the Hong Kong SAR Government, China, under grant number UGC/IDS/(R)11/19 & IDS(C)11/E01/20. Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",

year = "2022",

month = apr,

day = "30",

doi = "10.1117/12.2626131",

language = "English",

volume = "12177",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Masayuki Nakajima and Shogo Muramatsu and Jae-Gon Kim and Jing-Ming Guo and Qian Kemao",

booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2022",

address = "United States",

}

Wang, LW, Li, D, Siu, WC & Lun, DPK 2022, Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment. in M Nakajima, S Muramatsu, J-G Kim, J-M Guo & Q Kemao (eds), International Workshop on Advanced Imaging Technology, IWAIT 2022. vol. 12177, 1217725, Proceedings of SPIE - The International Society for Optical Engineering, vol. 12177, SPIE, 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022, Hong Kong, China, 4/01/22. https://doi.org/10.1117/12.2626131

Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment. / Wang, Li Wen; Li, Du; Siu, Wan Chi et al.
International Workshop on Advanced Imaging Technology, IWAIT 2022. ed. / Masayuki Nakajima; Shogo Muramatsu; Jae-Gon Kim; Jing-Ming Guo; Qian Kemao. Vol. 12177 SPIE, 2022. 1217725 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12177).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment

AU - Wang, Li Wen

AU - Li, Du

AU - Siu, Wan Chi

AU - Lun, Daniel Pak Kong

N1 - Funding Information: This work was supported by The Hong Kong Polytechnic University, Caritas Institute of Higher Education (ISG200206) and Research Grants Council of the Hong Kong SAR Government, China, under grant number UGC/IDS/(R)11/19 & IDS(C)11/E01/20. Publisher Copyright: © 2022 SPIE.

PY - 2022/4/30

Y1 - 2022/4/30

N2 - This paper gives focus on multi-lane detection from traffic cameras, which is based on automatic trajectory analysis and is promoted with advanced deep-learning technologies. Our proposed approach is based on big trajectory data that is robust to complex road scenes, which makes our approach particularly reliable and practical for Intelligent Transportation Systems. By using the deep learning object detection technology, it firstly generates big trajectory data on the road. Then, it detects the stop lines on the road and counts the number of lanes from the trajectories. Next, the trajectories are divided into different groups, where each group contains the trajectories of one lane. Finally, the lanes are fitted by the grouped trajectories. A large number of experiments have been done. The results show that the proposed approach can effectively detect the lanes on the road.

AB - This paper gives focus on multi-lane detection from traffic cameras, which is based on automatic trajectory analysis and is promoted with advanced deep-learning technologies. Our proposed approach is based on big trajectory data that is robust to complex road scenes, which makes our approach particularly reliable and practical for Intelligent Transportation Systems. By using the deep learning object detection technology, it firstly generates big trajectory data on the road. Then, it detects the stop lines on the road and counts the number of lanes from the trajectories. Next, the trajectories are divided into different groups, where each group contains the trajectories of one lane. Finally, the lanes are fitted by the grouped trajectories. A large number of experiments have been done. The results show that the proposed approach can effectively detect the lanes on the road.

KW - intelligent transportation system

KW - lane detection

KW - trajectory analysis

UR - http://www.scopus.com/inward/record.url?scp=85131840290&partnerID=8YFLogxK

U2 - 10.1117/12.2626131

DO - 10.1117/12.2626131

M3 - Conference article published in proceeding or book

AN - SCOPUS:85131840290

VL - 12177

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - International Workshop on Advanced Imaging Technology, IWAIT 2022

A2 - Nakajima, Masayuki

A2 - Muramatsu, Shogo

A2 - Kim, Jae-Gon

A2 - Guo, Jing-Ming

A2 - Kemao, Qian

PB - SPIE

T2 - 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022

Y2 - 4 January 2022 through 6 January 2022

ER -

Wang LW, Li D, Siu WC, Lun DPK. Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment. In Nakajima M, Muramatsu S, Kim JG, Guo JM, Kemao Q, editors, International Workshop on Advanced Imaging Technology, IWAIT 2022. Vol. 12177. SPIE. 2022. 1217725. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2626131

Robust Lane Detection through Automatic Trajectory Analysis with Deep Learning and Big Data Environment

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this