@inbook {48, title = {Detection and Recognition of Road Markings in Panoramic Images}, booktitle = {Computer Vision - ACCV 2014 Workshops}, series = {Lecture Notes in Computer Science}, volume = {9009}, year = {2015}, pages = {448-458}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, isbn = {978-3-319-16630-8}, doi = {10.1007/978-3-319-16631-5_33}, url = {http://dx.doi.org/10.1007/978-3-319-16631-5_33}, author = {Li, Cheng and Ivo M. Creusen and Lykele Hazelhoff and Peter H.N. de With}, editor = {Jawahar, C.V. and Shan, Shiguang} } @conference {vca2015, title = {Detection and recognition of road markings in panoramic images}, booktitle = {Proc. SPIE}, year = {2015}, abstract = {Detection of road lane markings is attractive for practical applications such as advanced driver assistance systems and road maintenance. This paper proposes a system to detect and recognize road lane markings in panoramic images. The system can be divided into four stages. First, an inverse perspective mapping is applied to the original panoramic image to generate a top-view road view, in which the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, a feature vector of each potential road marking segment is extracted by calculating the Euclidean distance between the center and the boundary at regular angular steps. Third, the shape of each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous falsely detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is promising and is capable of recognizing 93\%, 95\% and 91\% of striped line segments, blocks and arrows respectively, as well as 94\% of the lane markings.}, doi = {10.1117/12.2081395}, url = {http://dx.doi.org/10.1117/12.2081395}, author = {Li, Cheng and Ivo M. Creusen and Lykele Hazelhoff and Peter H.N. de With} }