Multi-Scale Convolutional Network for Person Re-identification

DOI

10.2991/cnct-16.2017.115How to use a DOI?

Keywords

Deep learning, Person re-identification.

Abstract

In the last several years, methods with learning procedure held the state-of-the-art results for person re-identification (re-id) problem, especially the metric learning algorithm. Recently, with the success of deep learning methods on many computer vision tasks, researchers started to put their focuses on learning high-performance features. In this paper, we propose a method by fusing features learned from a multi-scale convolutional neural network and the traditional hand-crafted features, which improves the performance significantly. The Shinpuhkan2014dataset has been chosen as the training set, and we evaluate the performances of the proposed method on VIPeR, PRID and i-LIDS. Experiments show that our method outperforms the existing methods and even approaches the performances of the methods which have a training step on the testing sets.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiong WU
PY  - 2016/12
DA  - 2016/12
TI  - Multi-Scale Convolutional Network for Person Re-identification
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 826
EP  - 835
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.115
DO  - 10.2991/cnct-16.2017.115
ID  - WU2016/12
ER  -