Triplet-based Deep Similarity Learning for Person Re-Identification

TL;DR

This paper introduces a triplet-based deep learning framework using CNNs for person re-identification, trained jointly on multiple datasets with a novel triplet sampling scheme, achieving competitive or superior results.

Contribution

The paper presents a new triplet-based deep similarity learning approach for person re-id, including a joint training method on multiple datasets and an effective triplet sampling scheme.

Findings

Effective deep feature learning for person re-id.

Joint training on multiple datasets improves generalization.

Outperforms or matches state-of-the-art methods on benchmarks.

Abstract

In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.