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.

该研究提出了一种名为 Deep-Person 的三分支框架，利用 LSTM 建模行人身体从头到脚的序列，提高局部特征的鉴别能力以及同时学习了鉴别嵌入和相似度测量。实验证明该框架在市场 - 1501，CUHK03 和 DukeMTMC-reID 等三个数据集上，超过了现有的最先进方法，单次查询设置下市场 - 1501 的 mAP 达到了 90.84％。

Deep-Person: 为人物再识别学习辨别性深度特征

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

We revisit two popular convolutional neural networks (CNN) in person
re-identification (re-ID), i.e, verification and classification models. The two
models have their respective advantages and limitations due to different loss
functions. In this paper, we shed light on how to combine the two models to
learn more discriminative pedestrian descriptors. Specifically, we propose a
new siamese network that simultaneously computes identification loss and
verification loss. Given a pair of training images, the network predicts the
identities of the two images and whether they belong to the same identity. Our
network learns a discriminative embedding and a similarity measurement at the
same time, thus making full usage of the annotations. Albeit simple, the
learned embedding improves the state-of-the-art performance on two public
person re-ID benchmarks. Further, we show our architecture can also be applied
in image retrieval.

本文旨在探讨如何将卷积神经网络的不同特性相融合，提高行人识别任务中的性能，并提出了一个新型的任务学习框架，取得了当前公开基准数据集上的最佳表现，并且还证明了这个框架能够适用于图像检索任务。