A Network Combining Local Features and Attention Mechanisms for Vehicle Re-Identification

Abstract

Vehicle of the same manufacturer and the same color can only be distinguished by their subtle difference. If these small features, such as stickers on windows and spray paint on cars, can be better used, we can significantly improve the accuracy of vehicle reidentification. This paper aims to develop an effective network combining local features and attention mechanisms for vehicle reidentification. It divides the feature map to enable the network to capture more detailed feature information. At the same time, it uses the attention mechanism to enable the network to focus on the most important part of each branch, effectively eliminating background and other interference, and improving the network performance. Experiments show that this method improves the result of Rank-1 and mAP on two public datasets: VeRi-776 and VRIC.