Deep Vision based Vehicle Retrieval for Automated Smart Traffic Surveillance Systems

Traffic systems form a significant part of a city and automated smart traffic surveillance systems have attracted significant research attention in recent years. Tasks such as vehicle detection, vehicle tracking, vehicle retrieval, anomaly detection in traffic flow, vehicle type detection, and other automated monitoring tasks are of essential importance in this ecosystem. In this work, we explore the problem of vision based vehicle retrieval, a major challenge in automated traffic surveillance systems in smart city environments. Vision based vehicle retrieval or vehicle re-identification is the process of identifying instances of the target vehicle given a gallery of vehicle images. We present a comprehensive review of existing works in this area, encompassing the evolution of vehicle retrieval models. We divide the existing work on vision based vehicle retrieval into four categories based on the techniques adopted and explore them in a systemic manner. Through this, we hope to provide the readers with an understanding of the techniques adopted in the existing works and how these can be further improved, the identified challenges and research gaps.