## 1 Introduction  
Nowadays, the security and safety of objects and public place is an important issue in society. Government and private sector organizations are trying to overcome this issue on a priority basis. Suppose we want to locate or to track a vehicle in a metropolitan city. In this situation, surveillance cameras play an important role, but continuous recording videos of surveillance cameras generate a large amount of data and it is hard for an operator to analyze the data whenever any specific incident happens. Therefore, an intelligent video surveillance system's goal is to automatically monitor and analyze the data to help an operator in understanding the acquired video of a surveillance camera [1,2]. Those cameras observe changes in pixels due to the object crossing from the field of view of a camera. According to this scenario, it is only implemented in those areas where there is a rare movement of vehicles or in a parking area [1,3]. The basic principle of cameras in a surveillance system is to detect, associate, and track a vehicle/person across the range of cameras at a number of locations with a different background. Object (Re-Id) aims to address the surveillance problem by matching the query image with a large number of gallery images that are captured over a non-overlapping camera network. Locating and tracking a vehicle on overlapping and non-overlapping surveillance cameras can be performed on the vehicle Re-Id system. First, multi-camera tracking is the tracking an individual vehicle using multiple cameras; the identity of the vehicle has been retrieved from the second camera based on the information obtained from the first camera. Second, if the locations of the camera are known, based on the Re-Id system, it is possible to track the moving path of a vehicle from one point to another. Moreover, in surveillance and security applications, Re-Id models can be employed to track the suspicious vehicle in a crime scene. Moreover, real time surveillance system is shown in Figure 1.  
Vehicle Re-Id gained more attention [4; 5] in the last two years. Before that, computer vision field researchers were more focused on person Re-Id applications [6]. Person Re-Id and vehicle Re-Id are almost two similar problems, but vehicle Re-Id is more challenging as compared to person Re-Id, due to the lack of appropriate datasets and images of the same vehicle usually having an overlapped appearance even with large viewpoint variations. A human body is usually upright, and the texture or color of the dress will not vary severely in different viewpoints [7]. However, the visual features of vehicle in varying views is totally different, as shown in Figure 2. For instance, there is no overlapping on the visual pattern of the vehicle across the front side and rear viewpoints. Conventional person Re-Id methods, in which multi-view processing is not a large consideration, are not reasonable to be transferred directly to vehicle Re-Id [8]. Figure 1: A glance of the practical environment where vehicles are captured by cameras in the city. Figure 2: Changing the viewpoint vehicle totally changes as compared to a person. Due to demand towards vehicle Re-Id in public and private sectors, there are several approaches proposed but broadly divided into two categories. In the first one, vehicle ID, location, and time information are continuously transmitted towards the control center, and the global positioning system (GPS) is used for tracking the shipment. After that, it is used for so many fields like tracking vehicles, vehicle anti-theft management systems, and intelligent transportation systems. In the second category, the vehicle is identified when it comes in the field of view of the device terminal. Surveillance cameras, radio-frequency identification, and inductive loop lie in this category [9].