Segmentation masks for real-time traffic sign recognition using weighted HOG-based trees

Traffic sign recognition is one of the main components of a Driver Assistance System (DAS). This paper presents a real-time traffic detection and classification approach of both circular and triangular signs. The system consists of three stages: 1) an image segmentation to reduce the search space, 2) a HOG-based Support Vector Machine (SVM) detection to extract both round and triangular traffic signs, and 3) a tree classifier (K-d tree or Random Forests) to identify the signs found. The methodology is tested on images under bad weather conditions and poor illumination. The image segmentation based on the enhancement of the red color channel improves the detection precision significantly achieving high recall rates and only a few false alarms. The tree classifiers also achieve high classification rates.