Real-time traffic sign recognition using spatially weighted HOG trees

Traffic sign recognition is one of the main components of a Driver Assistance System (DAS). This paper presents a real-time traffic sign recognition system. It consists of three stages: 1) an image segmentation using red color enhancement to reduce the search space, 2) a HOG-based Support Vector Machine (SVM) detection to extract the traffic signs, and 3) a tree classifier (K-d tree or Random Forests) to identify the signs found. This methodology is tested on images under bad weather conditions and poor illumination. The tree classifiers achieve high classification rates for the German Traffic Sign Recognition Benchmark and the ETH 80 dataset. The K-d tree classification is improved by introducing a Gaussian spatial weighting to favor the interior blocks of the HOG descriptors.