Road marking features extraction using the VIAPIX® system.

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Bibliographic Details
Title: Road marking features extraction using the VIAPIX® system.
Authors: Kaddah, W.1,2, Ouerhani, Y.1, Alfalou, A.2 ayman.al-falou@isen.fr, Desthieux, M.1, Brosseau, C.3, Gutierrez, C.1
Source: Optics Communications. Jul2016, Vol. 371, p117-127. 11p.
Subjects: Road markings, Optical correlation, Driver assistance systems, Robust control, Robotics
Abstract: Precise extraction of road marking features is a critical task for autonomous urban driving, augmented driver assistance, and robotics technologies. In this study, we consider an autonomous system allowing us lane detection for marked urban roads and analysis of their features. The task is to relate the georeferencing of road markings from images obtained using the VIAPIX® system. Based on inverse perspective mapping and color segmentation to detect all white objects existing on this road, the present algorithm enables us to examine these images automatically and rapidly and also to get information on road marks, their surface conditions, and their georeferencing. This algorithm allows detecting all road markings and identifying some of them by making use of a phase-only correlation filter (POF). We illustrate this algorithm and its robustness by applying it to a variety of relevant scenarios. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Precise extraction of road marking features is a critical task for autonomous urban driving, augmented driver assistance, and robotics technologies. In this study, we consider an autonomous system allowing us lane detection for marked urban roads and analysis of their features. The task is to relate the georeferencing of road markings from images obtained using the VIAPIX® system. Based on inverse perspective mapping and color segmentation to detect all white objects existing on this road, the present algorithm enables us to examine these images automatically and rapidly and also to get information on road marks, their surface conditions, and their georeferencing. This algorithm allows detecting all road markings and identifying some of them by making use of a phase-only correlation filter (POF). We illustrate this algorithm and its robustness by applying it to a variety of relevant scenarios. [ABSTRACT FROM AUTHOR]
ISSN:00304018
DOI:10.1016/j.optcom.2016.03.065