Learning local embedding deep features for person re-identification in camera networks

Zhong Zhang,Meiyan Huang

doi:10.1186/s13638-018-1101-x

Abstract

In this paper, we propose a novel feature learning method named local embedding deep features (LEDF) for person re-identification in camera networks. In order to learn the structural information of pedestrian, we first utilize the verification network that does not require explicit identity labels to obtain the local summing maps. We then combine all local summing maps of a pedestrian image to form the holistic summing map which has the same identity label with the original pedestrian image. Finally, we take the holistic summing maps as the input to train the identification network, and then obtain the LEDF from the last fully connected layer. The proposed LEDF fully considers the structural information by learning the local features and meanwhile possesses strong discriminative ability by learning global features. The experimental results on two large-scale datasets (Market-1501 and CUHK03) demonstrate that the proposed LEDF achieves better results than the state-of-the-art methods.

Highlights

We propose a novel feature learning method named local embedding deep features (LEDF) for person re-identification in sensor networks which train an identification network in a local way
5 Conclusions In this paper, we have proposed a novel feature learning method named LEDF for person re-identification in camera networks, which trains the identification network in a local way
We learn the local features by dividing each pedestrian image into several regions, and take pairs of regions as the input of the verification network to extract local summing maps

Summary

Introduction

The performance of person re-identification in sensor networks is closely related to many other applications, such as person retrieval, behavior analysis, long-term person tracking, and so on [5]. Person re-identification in sensor networks is a very challenging problem for two reasons. The same person observed in different cameras often undergoes high variations in illumination, poses, viewpoints, and occlusions. A surveillance camera in the public captures hundreds of pedestrians within 1 day, and some of them have similar appearance. The core of person re-identification is to find a discriminative representation and a good metric to evaluate the similarity between pedestrian images

Methods

Results

Conclusion