Abstract: Based on the SIFT feature, two kinds of middle-level features were proposed for vehicle type classification from vehicle's frontal view images. Structural feature distribution characterizing the spatial relative layout of vehicle parts helps to distinguish different types of vehicles from the background, and the appearance-based feature distribution is local and robust to the interference of illumination variation and the background. These two kinds of distributions were combined to classify vehicle types by multiple kernel learning. The experimental results demonstrate that our method is robust to various illumination and background interference.