Deep-Person: Learning Discriminative Deep Features for Person Re-Identification

TL;DR

Deep-Person introduces a novel end-to-end framework that models pedestrian body parts sequentially with LSTM, integrating local and global features for improved person re-identification accuracy.

Contribution

The paper presents a three-branch deep network combining LSTM-based part modeling, local-global feature integration, and joint identification and ranking tasks for person Re-ID.

Findings

Outperforms state-of-the-art on Market-1501, CUHK03, DukeMTMC-reID datasets.

Achieves 90.84% mAP on Market-1501 with re-ranking.

Effectively models spatial context between body parts.

Abstract

Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor to each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the contextual information strengthens the discriminative ability of local representation. We also leverage the complementary information between local and global feature. Furthermore, we integrate both identification task and ranking task in one network, where a discriminative embedding and a similarity measurement are learned concurrently. This results in a novel three-branch framework named Deep-Person, which learns highly discriminative features for person Re-ID. Experimental results demonstrate that Deep-Person outperforms the state-of-the-art methods by a large margin on three challenging datasets including Market-1501, CUHK03, and DukeMTMC-reID. Specifically, combining with a re-ranking approach, we achieve a 90.84% mAP on Market-1501 under single query setting.