Vehicle type classification using distributions of structural and appearance-based features

Classifying vehicle types from an image is a challenging task due to various light conditions and background interferences. We present a vehicle type classification method using structural and appearance-based features in this paper. The structural feature which characterizes the spatial relative layouts of vehicle parts helps to distinguish a vehicle from the background, and the appearance-based feature is local and robust to the interferences of illumination variation and the background. To obtain compact and discriminative representations of vehicles, the distributions of these two types of features are computed. We further employ Multiple Kernel Learning to combine multiple distributions together for classifying vehicle types. Experimental results demonstrate the effectiveness of our method.