2015/09/03 by Caio César Teodoro Mendes, Mendes, Caio César Teodoro, Vincent Frémont +4
Computer Science · Engineering · #Automated Road and Building Extraction #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Object Detection Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1509.01122
arxiv created 2015/09/03 · openalex publication_date 2015/09/03 · arxiv updated 2015/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Road detection is a fundamental task in autonomous navigation systems. In this paper, we consider the case of monocular road detection, where images are segmented into road and non-road regions. Our starting point is the well-known machine learning approach, in which a classifier is trained to distinguish road and non-road regions based on hand-labeled images. We proceed by introducing the use of "contextual blocks" as an efficient way of providing contextual information to the classifier. Overall, the proposed methodology, including its image feature selection and classifier, was conceived with computational cost in mind, leaving room for optimized implementations. Regarding experiments, we perform a sensible evaluation of each phase and feature subset that composes our system. The results show a great benefit from using contextual blocks and demonstrate their computational efficiency. Finally, we submit our results to the KITTI road detection benchmark achieving scores comparable with state of the art methods.