Detection and recognition of road markings in panoramic images

Abstract

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.

Combined generation of road marking and road sign databases applied to consistency checking of pedestrian crossings

“…The road-marking recognition system processes all panoramic images captured within a region of interest and follows a similar architecture as the recognition systems described in literature [6,7,8,9]. Figure 4 displays the system overview, of which each main system component is summarized below.…”

“…Most of such systems focus on the recognition of signs in single images, while only few also identify their 3D positions [2,3]. Road-marking recognition systems can be based on LiDAR [5] or can exploit street-level images [6,7,8,9]. Combined generation of such databases is less commonly explored, although Choi et al [10] describe the combination of crosswalk and traffic light detection.…”

Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection

“…Yang [14] addressed the problem of recognizing the room structure from a 360°cylindrical panorama by transforming the original panorama into sub-images projected from four different perspectives. An algorithm has also been proposed that can detect and recognize road lane markings from panoramic images [15].…”