Traffic Light Detection and Recognition based on Haar-like Features

Abstract

The problem of traffic light detection and recognition is investigated in this paper. Most algorithms used in traffic light detection and recognition are based on color detection. The color-based approach has some difficulties in that if the color of the traffic lights is changed by external factors, they will not be recognized and errors will occur. We propose an algorithm for traffic light detection and recognition based on Haar-like features in this paper. We use Haar-like features to learn about the traffic light image and detect the candidate area based on the learning data. The detected candidate image is verified by the pre-learned SVM(Support Vector Machine) classifier, and binarization and morphology operations are performed on the verified candidate image for detection of the traffic light object. The detected traffic light is divided into respective signal areas to determine the current on/off status of traffic lights. The signal signs in the respective areas are defined by regulation and the sign of traffic lights can be recognized by recognizing on/off of the signals in the respective areas. The experimental study is performed to show that it is possible to detect and recognize traffic lights irrespective of color changes.