This paper proposes a fast method for detecting symbolic road markings (SRMs) and stop-lines. The proposed method efficiently restricts the search area based on the lane detection results and finds SRMs and stop-lines in a cost-effective manner. The SRM detector generates multiple SRM candidates using a top-hat filter and projection histogram and classifies their types using a histogram of oriented gradient (HOG) feature and total error rate (TER)-based classifier. The stop-line detector creates stop-line candidates via random sample consensus (RANSAC)-based parallel line pair estimation and verifies them using the HOG feature and TER-based classifier. The proposed method achieves reasonable detection rates and extremely low false positive rates along with a fast computing time.
Fast symbolic road marking and stop-line detection for vehicle localization
2015-06-01
983633 byte
Conference paper
Electronic Resource
English
Enhancing vehicle localization by matching HD map with road marking detection
SAGE Publications | 2024
|Symbolic Road Marking Recognition Using Convolutional Neural Networks
British Library Conference Proceedings | 2017
|ROAD MARKING DETECTION METHOD AND ROAD MARKING DETECTION DEVICE
European Patent Office | 2024
|Marking line detection system and marking line detection method of a distant road surface area
European Patent Office | 2017
|