Radial basis function network for traffic scene classification in single image mode

In this paper, a radial basis function (RBF) network based method inspired by mature algorithms for face recognition is applied to classify traffic scenes in single image mode. Not to follow traditional ways of estimating traffic states through image segmentation and vehicle tracking, this method avoids complicated problems in digital image processing (DIP) and can operate on just one image, while the old ones rely on consecutive images. The proposed method adopts discrete cosine transform (DCT) for feature selection, then a supervised clustering algorithm is fulfilled to help design hidden layer of RBF network for which Gaussian function is chosen, finally linear least square (LLS) is used to solve the weights training problem. Experiments show that this method is valid and effective under the new application background.