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Discovering Multi-relational Latent Attributes by Visual Similarity Networks Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Cross Dataset Person Re-identification

Authors : Yang Hu, Dong Yi, Shengcai Liao, Zhen Lei, Stan Z. Li

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

Until now, most existing researches on person re-identification aim at improving the recognition rate on single dataset setting. The training data and testing data of these methods are form the same source. Although they have obtained high recognition rate in experiments, they usually perform poorly in practical applications. In this paper, we focus on the cross dataset person re-identification which make more sense in the real world. We present a deep learning framework based on convolutional neural networks to learn the person representation instead of existing hand-crafted features, and cosine metric is used to calculate the similarity. Three different datasets Shinpuhkan2014dataset, CUHK and CASPR are chosen as the training sets, we evaluate the performances of the learned person representations on VIPeR. For the training set Shinpuhkan2014dataset, we also evaluate the performances on PRID and iLIDS. Experiments show that our method outperforms the existing cross dataset methods significantly and even approaches the performances of some methods in single dataset setting.

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previous chapter Gait-Assisted Person Re-identification in Wide Area Surveillance
next chapter Spatio-Temporal Consistency for Head Detection in High-Density Scenes
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Metadata
Title
Cross Dataset Person Re-identification
Authors
Yang Hu
Dong Yi
Shengcai Liao
Zhen Lei
Stan Z. Li
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16633-9

Electronic ISBN: 978-3-319-16634-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16634-6_47

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