Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification

TL;DR

This paper introduces an unsupervised self-aligned network for UAV vehicle re-identification that effectively extracts fine-grained features without requiring detailed annotations, improving performance on UAV-specific datasets.

Contribution

The proposed method develops a self-alignment module and integrates spatial and global features, addressing UAV vehicle ReID challenges without extensive annotations.

Findings

Achieves state-of-the-art performance on UAV-VeID dataset.

Effectively handles variable orientations in UAV imagery.

Improves feature discrimination for similar-looking vehicles.

Abstract

Compared with existing vehicle re-identification (ReID) tasks conducted with datasets collected by fixed surveillance cameras, vehicle ReID for unmanned aerial vehicle (UAV) is still under-explored and could be more challenging. Vehicles with the same color and type show extremely similar appearance from the UAV's perspective so that mining fine-grained characteristics becomes necessary. Recent works tend to extract distinguishing information by regional features and component features. The former requires input images to be aligned and the latter entails detailed annotations, both of which are difficult to meet in UAV application. In order to extract efficient fine-grained features and avoid tedious annotating work, this letter develops an unsupervised self-aligned network consisting of three branches. The network introduced a self-alignment module to convert the input images with variable orientations to a uniform orientation, which is implemented under the constraint of triple loss function designed with spatial features. On this basis, spatial features, obtained by vertical and horizontal segmentation methods, and global features are integrated to improve the representation ability in embedded space. Extensive experiments are conducted on UAV-VeID dataset, and our method achieves the best performance compared with recent ReID works.