A Novel Cascading Scheme to Improve Speed and Accuracy of a VMMR System

In the last decade, many researches have been done on fine-grained recognition. The main category of the object is known in this problem and the goal is to determine the subcategory or fine-grained category. Vehicle Make and Model Recognition (VMMR) is a hard fine-grained classification problem, due to the large number of classes, substantial inner-class and small inter-class distance. Furthermore, improving system accuracy leads to increasing in processing time. As we can see the state-of-the-art machine vision tool like convolutional neural networks lacks in real-time processing time. In this paper, a method has been presented briefly for VMMR firstly. Secondly, a cascading scheme for improving both speed and accuracy of this VMMR system has been proposed. In order to eliminate extra processing cost, the proposed cascading scheme applies classifiers to the input image in a sequential manner. Some effective criterions for an efficient ordering of classifiers are proposed and finally a fusion of them is used in the cascade algorithm. For evaluation purposes, a new dataset with more than 5000 vehicles of 28 different makes and models has been collected. The experimental results on this dataset and comprehensive CompCars dataset show outstanding performance of our approach. Our cascading scheme results up to 80% increase in the system processing speed.