In this paper, we propose and evaluate unexplored approaches for real-time automated vehicle make and model recognition (VMMR) based on a bag of speeded-up robust features (BoSURF) and demonstrate the suitability of these approaches for vehicle identification systems. The proposed approaches use SURF features of vehicles' front-or rear-facing images and retain the dominant characteristic features (codewords) in a dictionary. Two schemes of dictionary building are evaluated: "single dictionary" and "modular dictionary." Based on the optimized dictionaries, the SURF features of vehicles' front-or rear-face images are embedded into BoSURF histograms, which are used to train multiclass support vector machines (SVMs) for classification. Two real-time VMMR classification schemes are proposed and evaluated: a single multiclass SVM and an ensemble of multiclass SVM based on attribute bagging. The processing speed and accuracy of the VMMR system are affected greatly by the size of the dictionary. The tradeoff between speed and accuracy is studied to determine optimal dictionary sizes for the VMMR problem. The effectiveness of our approaches is demonstrated through cross-validation tests on a recent publicly accessible VMMR data set. The experimental results prove the superiority of our work over the state of the art, in terms of both processing speed and accuracy, making it highly applicable to real-time VMMR systems.

Automated Vehicle Classification (AVC) based on vision sensors has received active attention from researchers, due to heightened security concerns in Intelligent Transportation Systems. In this work, we propose a categorization of AVC studies based on the granularity of classification, namely Vehicle Type Recognition, Vehicle Make Recognition, and Vehicle Make and Model Recognition. For each category of AVC systems, we present a comprehensive review and comparison of features extraction, global representation, and classification techniques. We also present the accuracy and speed-related performance metrics and discuss how they can be used to compare and evaluate different AVC works. The various datasets proposed over the years for AVC are also compared in light of the real-world challenges they represent, and those they do not. The major challenges involved in each category of AVC systems are presented, highlighting open problems in this area of research. Finally, we conclude by providing future directions of research in this area, paving the way toward efficient large-scale AVC systems. This survey shall help researchers interested in the area to analyze works completed so far in each category of AVC, focusing on techniques proposed for each module, and to chalk out strategies to enhance state-of-the-art technology.

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