Vehicle Re-identification Based on Multi-Scale Attention Feature Fusion

Abstract

It is important to extract vehicle appearance features for vehicle re-identification. The appearance variation of the same vehicle from different viewpoints and the appearance similarity between vehicles from different classes bring challenges for capturing the descriptive features. Considering these, we propose a multi-scale attention feature fusion network (MSAF) for vehicle re-identification. It uses ResNet50 as the backbone, and introduces a scalable channel attention module for each feature channel. Then a multi-scale fusion module is designed to output the final extracted vehicle features. Experimental results on the VERI-Wild dataset indicate that the proposed MSAF achieves high Rank-1 index of 91.20% with mAP of 80.20%.