Using Discriminative Parts for Vehicle Make and Model Recognition

In fine-grained recognition, the main category of object is well known and the goal is to determine the subcategory or fine-grained category. Vehicle make and model recognition (VMMR) is a fine-grained classification problem. It includes several challenges like the large number of classes, substantial inner-class and small inter-class distance. VMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. VMMR can also be used when specific models of vehicles are required to be automatically identified by cameras. Few methods have been proposed to cope with limited lighting conditions. A number of recent studies have shown that latent SVM trained on a large-scale dataset using data mining can achieve impressive results on several object classification tasks. In this paper, a novel method has been proposed for VMMR using a modified version of latent SVM. This method finds discriminative parts of each class of vehicles automatically and then learns a model for each class using features extracted from these parts and spatial relationship between them. The parts weights of each model are tuned using training dataset. Putting this individual models together, our proposed system can classify vehicles make and model. All training and testing steps of the proposed system are done automatically. For training and testing the performance of the system, a new dataset including more than 5000 vehicles of 28 different make and models has been collected. This dataset poses different kind of challenges, including variations in illumination and resolution. The experimental results performed on this dataset show the high accuracy of our system.