A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition

Abstract

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS) and corresponding components such as Automated Vehicular Surveillance (AVS). A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multiclass classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicle makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system.,,,,,, In this paper, facing the growing importance of make and model recognition of vehicles, we present an image dataset1 with 9; 170 different classes of vehicles to advance the corresponding tasks. Extensive experiments conducted using baseline approaches yield superior results for images that were occluded, under low illumination, partial or nonfrontal camera views, available in our VMMR dataset. The approaches presented herewith provide a robust VMMR system for applications in realistic environments.