  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.
