A Cascaded Part-Based System for Fine-Grained Vehicle Classification

Abstract

Vehicle make and model recognition (VMMR) has become an important part of intelligent transportation systems. VMMR can be useful when license plate recognition is not feasible or fake number plates are used. VMMR is a hard, fine-grained classification problem, due to the large number of classes, substantial inner-class, and small inter-class distance. A novel cascaded part-based system has been proposed in this paper for VMMR. This system uses latent support vector machine formulation for automatically finding the discriminative parts of each vehicle category. At the same time, it learns a part-based model for each category. Our approach employs a new training procedure, a novel greedy parts localization, and a practical multi-class data mining algorithm. In order to speed up the system processing time, a novel cascading scheme has been proposed. This cascading scheme applies classifiers to the input image in a sequential manner, based on the two proposed criteria: confidence and frequency. The cascaded system can run up to 80% faster with analogous accuracy in comparison with the non-cascaded system. The extensive experiments on our data set and the CompCars data set indicate the outstanding performance of our approach. The proposed approach achieves an average accuracy of 97.01% on our challenging data set and an average accuracy of 95.55% on CompCars data set.