Abstract:
Vehicle Make and Model Classification (VMMC) 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 VMMC system significantly reduces the overhead cost of resources
otherwise required. The VMMC 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 VMMC system.
In this thesis, facing the growing importance of make and model classification of
vehicles, we present an image dataset with 94 different classes containing 129,000 images of
vehicles in Pakistan to advance the corresponding tasks. Extensive experiments conducted
using baseline approaches yield superior results for images that were occluded, under low
illumination, partial and overhead camera views, available in our VMMC dataset. The
approaches presented herewith provide a robust VMMC system for applications in realistic
environments.
