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A Multilane Tracking Algorithm Using IPDA with Intensity Feature
Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consisten...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826513/ https://www.ncbi.nlm.nih.gov/pubmed/33440714 http://dx.doi.org/10.3390/s21020461 |
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author | Akbari, Behzad Thiyagalingam, Jeyan Lee, Richard Thia, Kirubarajan |
author_facet | Akbari, Behzad Thiyagalingam, Jeyan Lee, Richard Thia, Kirubarajan |
author_sort | Akbari, Behzad |
collection | PubMed |
description | Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as [Formula: see text] , [Formula: see text] , and [Formula: see text] improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained. |
format | Online Article Text |
id | pubmed-7826513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78265132021-01-25 A Multilane Tracking Algorithm Using IPDA with Intensity Feature Akbari, Behzad Thiyagalingam, Jeyan Lee, Richard Thia, Kirubarajan Sensors (Basel) Article Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as [Formula: see text] , [Formula: see text] , and [Formula: see text] improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained. MDPI 2021-01-11 /pmc/articles/PMC7826513/ /pubmed/33440714 http://dx.doi.org/10.3390/s21020461 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Akbari, Behzad Thiyagalingam, Jeyan Lee, Richard Thia, Kirubarajan A Multilane Tracking Algorithm Using IPDA with Intensity Feature |
title | A Multilane Tracking Algorithm Using IPDA with Intensity Feature |
title_full | A Multilane Tracking Algorithm Using IPDA with Intensity Feature |
title_fullStr | A Multilane Tracking Algorithm Using IPDA with Intensity Feature |
title_full_unstemmed | A Multilane Tracking Algorithm Using IPDA with Intensity Feature |
title_short | A Multilane Tracking Algorithm Using IPDA with Intensity Feature |
title_sort | multilane tracking algorithm using ipda with intensity feature |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826513/ https://www.ncbi.nlm.nih.gov/pubmed/33440714 http://dx.doi.org/10.3390/s21020461 |
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