Two decades of vehicle make and model recognition – Survey, challenges and future directions

Abstract

Vehicle make and model recognition (VMMR) is a crucial task for developing automatic vehicle recognition (AVR) systems, and has gained significant attention in the fields of computer vision and artificial intelligence in recent years. The ability to automatically identify a vehicle's make and model has numerous practical applications, such as traffic monitoring, vehicle re-identification, etc. This survey paper provides a comprehensive overview of the state-of-the-art techniques developed for VMMR problem. The survey begins with an introduction to the problem of AVR, followed by a discussion of the various factors that affect the accuracy of recognition, including lighting conditions, viewpoint variations, and occlusions. We then discuss a solution to this problem and provide an overview of the different approaches for VMMR, such as machine learning approaches and deep learning approaches. This survey also provides a comprehensive review of publicly available datasets that have been used for evaluating VMMR methods. Finally, the paper concludes with a discussion of some of the remaining challenges in VMMR, such as the need for large-scale datasets with more diverse vehicle models, the need for more robust methods that can handle variations in lighting and viewpoint, and the need for real-time methods that can operate in a variety of settings. This survey aims to serve as a valuable resource for researchers working in the field of computer vision that includes AVR.

Keywords

• Vehicle Recognition
• Make and Model
• Traffic Management
• Machine Learning
• Deep Learning