Investigation of Directional Traffic Sign Feature Extracting Based on PCNN in Different Color Space

For the first time in this paper, directional traffic signs feature extracting based on Pulse Coupled Neural Network (PCNN) in different color space are investigated. Entropy series is extracted from the image of traffic sign in both RGB model and HSV model. Each entropy series of R, G, B, H, S, V color space is used as feature vector for recognition, match analysis is carried out by minimum variance. Experiments are carried out based on the directional signs class in national standard GB5768-1999 database. Experiment results show that feature vector based on Entropy series in B color space get the higher recognition rates than the other color space, with 50 iteration and 5 × 5 convolution kernel matrix of PCNN.