Lane detection using spline model

Wang, Yue; Shen, Dinggang; Teoh, Eam Khwang

doi:10.1016/s0167-8655(00)00021-0

Cited by 250 publications

(124 citation statements)

References 16 publications

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“…Computation can be largely simplified in special applications. Taking the lane detection as an example, the biological principles are to detect and recognize a line, which can work well even if the lanes are partly missing [62][63][64], as seen in Figure 6.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to +1 , = 1, 2, . . .) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

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Section: Simulation and Discussionmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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“…Finally and third, from each segment a feature vector is extracted, and a Support Vector Machine (SVM) classifier is employed to distinguish segments based on their geometric features. Since some background segments can have similar shapes as road markings, we model the lanes that appear in the image, using RANSAC and a Catmull-Rom spline [10] with the lane marking candidates classified by SVM. Many non-road marking segments can be rejected based on the lane positions and the orientation of the segments (the orientation should be consistent with the orientation of the lane markings).…”

Section: Road Marking Detection and Recognitionmentioning

confidence: 99%

“…For this purpose, we model lanes, based on lane marking candidates classified by the SVM. Since lanes can appear both straight and curved in the top-view image, two models are applied consecutively: RANSAC to model straight lanes and Catmull-Rom spline to interpolate curved lanes [10]. First, by default we search to fit a straight line using RANSAC.…”

Section: Lane Modelingmentioning

confidence: 99%

Detection and recognition of road markings in panoramic images

Creusen

Hazelhoff

2015

SPIE Proceedings

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Creusen, I.M.; Hazelhoff, L.; de With, P.H.N. Published in:Proceedings of the SPIE Elecronic Imaging, Video Surveillance and Transportation Imaging Applications, San Francisco, California, USA, February 8-12, 2015 Published: 01/01/2015 Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract. The detection of road lane markings has many practical applications, such as advanced driver assistance systems and road maintenance. In this paper we propose an algorithm to detect and recognize road lane markings from panoramic images. Our algorithm consists of four steps. First, an inverse perspective mapping is applied to the 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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“…이어서, 밝고 어두운 영역의 평균 들의 차이로 노면의 상태를 판단하고, 노면의 상태에 따라 Canny 검출에 사용되는 파라미터의 값을 조절하여 에지를 검출한다 [4] . 획득한 에지 정보로부터 Hough 변환을 이용하 여 소실점을 구하고, 소실점 하부에 있는 영역을 6개로 분 할하여 Catmull Rom Spline 을 구한다 [5] . 본 논문의 특징은 Canny 검출 시 적용되는 파라미터 값을 Otsu 방법을 통하 여 환경 변화에 자동적으로 획득함으로써 환경 변화에 강 건한 에지 검출을 할 수 있도록 하는 것이다.…”

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Lane Spline Generation Using Edge Detection Robust to Environmental Changes

Kwon¹,

Shin²

2012

Journal of Broadcast Engineering

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Lane detection with the use of a camera is an essential task required for the development of advanced driving assistance system. In this paper, edges of the lane are generated by applying Canny's method. The edge detection usually makes different results for several environmental conditions depending on the clearness of lane quality, so that it sometimes causes wrong lane detection. Therefore, we propose robust algorithm to environmental changes that automatically adjusts parameter for edge detection and generates edges more stably. Based on the acquired edges, we finally generate the spline curve of lane by using Catmull-Rom spline.

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Lane detection using spline model

Cited by 250 publications

References 16 publications

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Detection and recognition of road markings in panoramic images

Lane Spline Generation Using Edge Detection Robust to Environmental Changes

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