Lightweight Multi-Branch Network for Person Re-Identification

TL;DR

This paper introduces a lightweight, multi-branch neural network for person re-identification that combines global, part-based, and channel features, achieving high accuracy with reduced complexity.

Contribution

It presents a novel multi-branch architecture built on OSNet that effectively integrates multiple feature types for improved re-identification performance.

Findings

Achieves state-of-the-art results on CUHK03 and Market-1501 datasets.

Maintains high accuracy with a lightweight model.

Demonstrates the effectiveness of combining multiple feature types.

Abstract

Person Re-Identification aims to retrieve person identities from images captured by multiple cameras or the same cameras in different time instances and locations. Because of its importance in many vision applications from surveillance to human-machine interaction, person re-identification methods need to be reliable and fast. While more and more deep architectures are proposed for increasing performance, those methods also increase overall model complexity. This paper proposes a lightweight network that combines global, part-based, and channel features in a unified multi-branch architecture that builds on the resource-efficient OSNet backbone. Using a well-founded combination of training techniques and design choices, our final model achieves state-of-the-art results on CUHK03 labeled, CUHK03 detected, and Market-1501 with 85.1% mAP / 87.2% rank1, 82.4% mAP / 84.9% rank1, and 91.5% mAP / 96.3% rank1, respectively.