In this paper, we present a stereo-vision based approach for road sign detection. As opposed to traffic signs, which are typically made up of well-defined pictographs, road signs can contain arbitrary information. Here, color and shape are the main two cues that represent different classes of road signs, e.g. signs on the highway vs. signs on country roads. To that extent, the proposed model couples efficient low-level color-based segmentation in HSL space with higher-level constraints that integrate prior knowledge on sign geometry in 3D through stereo-vision. Additional robustness is obtained by temporal integration as well as by matching detected signs against the results of object detectors for other traffic participants. The effectiveness of our approach is demonstrated on a real-world stereo-vision dataset (3700 images) that has been captured from a moving vehicle on German highways and country roads. Our results indicate competitive performance at real-time speeds.
Vision-Based Road Sign Detection
2015-09-01
410966 byte
Conference paper
Electronic Resource
English
Road sign detection and reading using machine vision technique
Automotive engineering | 1993
|93ME107 Road Sign Detection and Reading Using Machine Vision Technique
British Library Conference Proceedings | 1993
|ROAD SIGN DETECTION DEVICE, ROAD SIGN DETECTION METHOD, PROGRAM, AND ROAD SURFACE DETECTION DEVICE
European Patent Office | 2018
|Road traffic sign recognition system based on machine vision
European Patent Office | 2021
|ROAD SURFACE SIGN DETECTION APPARATUS AND ROAD SURFACE SIGN DETECTION METHOD
European Patent Office | 2017
|