An Efficient Real-Time Vehicle Re-Identification Scheme Using Urban Surveillance Videos

With the explosive use of surveillance and onboard cameras, vision-based Vehicle Re-identification (VReID) has attracted widespread attention. The goal of VReID is to search and identify the target vehicle from a large number of images. An efficient VReID model can help the police make fast decisions and improve regional security. The major challenge of the VReID is to distinguish the subtle visual difference between different vehicles. In this work, we propose a Compact Attention Unit (CAU) that relies on a single attention map to extract the discriminative local features of the vehicle. We add two CAUs to the truncated ResNet to construct a small but efficient VReID model, ResNetT-CAU. The feature representation of the vehicle image is the concatenation of the extracted global and local features. Compared with the original ResNet, the model size of ResNetT-CAU is reduced by 60% and has excellent VReID performance. We conduct experiments on two benchmark datasets, VeRi and VehicleID. The results show the proposed model stably achieves excellent VReID performance with very fast processing speed.

I. Introduction

Vehicle Re-identification (VReID) is an essential topic in the Intelligent Transportation System (ITS) and Smart City [1]–[3]. With the explosive use of surveillance and onboard cameras, the vision-based VReID model has attracted a lot of attention. As shown in Fig. 1, given a query vehicle image, the goal of the vision-based VReID is to find the target vehicle from a large image database. With the VReID system’s help, stolen vehicles can be fast located and tracked. By deploying a small VReID system on the vehicles such as Unmanned Aerial Vehicle (UAV), the vehicle can directly process the images captured by the onboard camera. With the help of the vehicular network and the vehicle-to-every technique [4], [5], when a possible match is found, the information is fast-forwarded to a target agent such as a traffic control center or the police station; which will significantly help make a fast decision and improve the regional security.