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Traffic Sign Recognition, and Tracking Using RANSAC-Based Motion Estimation for Autonomous Vehicles
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 Title & Authors
Traffic Sign Recognition, and Tracking Using RANSAC-Based Motion Estimation for Autonomous Vehicles
Kim, Seong-Uk; Lee, Joon-Woong;
 
 Abstract
Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.
 Keywords
autonomous vehicle;traffic sign recognition;HOG;SVM;tracking;motion estimation;
 Language
Korean
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