Triplet-Based Deep Similarity Learning for Person Re-Identification

Wentong Liao, Michael Ying Yang, Ni Zhan, Bodo Rosenhahn; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 385-393

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.

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[bibtex]
@InProceedings{Liao_2017_ICCV,
author = {Liao, Wentong and Ying Yang, Michael and Zhan, Ni and Rosenhahn, Bodo},
title = {Triplet-Based Deep Similarity Learning for Person Re-Identification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}