Vehicle Re-Identification and Tracking: Algorithmic Approach, Challenges and Future Directions

Abstract

Vehicle re-identification and tracking play a vital role in intelligent transportation systems as they enhance traffic management, improve safety, and optimize flow by precisely monitoring and analyzing vehicle movements across various locations. This technology enables the collecting of data in real-time, which allows for effective identification of incidents, enforcement of laws, and decision-making in urban planning. Deep learning techniques used in vehicle re-identification extract distinct characteristics to identify and match a vehicle across different camera perspectives. This bridges the non-overlapping field of camera views and forms a relationship between the detected vehicles. Tracking enhances this process by assigning a distinct identifier to the recognized vehicle, allowing for the creation of a continuous trajectory across the network for further analysis. Vehicle re-identification and tracking have made substantial progress in recent years as a result of the accelerated development of deep learning. Consequently, it is imperative to conduct a thorough examination of these chores. To provide a detailed picture of the research towards vehicle re-identification and tracking, this study provides the recent advancements of various datasets, and frameworks and strategies undertaken to perform these tasks. Specifically, the paper provides a comprehensive review of the different modes of re-identification of vehicles and further analysis. The paper also discusses the challenges and directions that can be taken in future for vehicle re-identification and tracking.