Classification and identification of vehicle type and make by cortex-like image descriptor HMAX

Identification of vehicle types from images is a challenging task. Though several methods have been proposed, most of the works has been done in strictly controlled conditions, where the feature information was calculated on ad hoc bases. In order to achieve better performance of make and model recognition, we emphasised the importance of feature description in a principled way from a biologically inspired vision modelling perspective. As a visual feature expression model in cortex, HMAX integrates general beliefs about the visual system in a quantitative framework. We applied HMAX for vehicle type recognition using a database that includes over 2,000 vehicle images of 26 classes recorded from surveillance cameras involving various complex photographic conditions. Experimental results using the HMAX model and multi-layer perceptron (MLP) offers a classification rate of 94% and averaged identification accuracy of 95%, which is higher than other commonly used classification algorithms such as kNN and SVM.