Global-Local Dynamic Feature Alignment Network for Person Re-Identification

TL;DR

This paper introduces a novel dynamic feature alignment network for person re-identification that effectively handles spatial misalignments without extra supervision, improving accuracy across multiple datasets.

Contribution

The paper proposes the Local Sliding Alignment (LSA) strategy and integrates it into a global-local network framework, enhancing feature alignment and re-identification accuracy.

Findings

Achieves 86.1% mAP on Market-1501

Attains 94.8% Rank-1 accuracy on Market-1501

Demonstrates competitive performance on multiple datasets

Abstract

The misalignment of human images caused by bounding box detection errors or partial occlusions is one of the main challenges in person Re-Identification (Re-ID) tasks. Previous local-based methods mainly focus on learning local features in predefined semantic regions of pedestrians. These methods usually use local hard alignment methods or introduce auxiliary information such as key human pose points to match local features, which are often not applicable when large scene differences are encountered. To solve these problems, we propose a simple and efficient Local Sliding Alignment (LSA) strategy to dynamically align the local features of two images by setting a sliding window on the local stripes of the pedestrian. LSA can effectively suppress spatial misalignment and does not need to introduce extra supervision information. Then, we design a Global-Local Dynamic Feature Alignment Network (GLDFA-Net) framework, which contains both global and local branches. We introduce LSA into the local branch of GLDFA-Net to guide the computation of distance metrics, which can further improve the accuracy of the testing phase. Evaluation experiments on several mainstream evaluation datasets including Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 show that our method has competitive accuracy over the several state-of-the-art person Re-ID methods. Specifically, it achieves 86.1% mAP and 94.8% Rank-1 accuracy on Market1501.