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Erschienen in:
Visual Tracking by Local Superpixel Matching with Markov Random Field Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Deep Similarity Feature Learning for Person Re-identification

verfasst von : Yanan Guo, Dapeng Tao, Jun Yu, Yaotang Li

Erschienen in: Advances in Multimedia Information Processing - PCM 2016

Verlag: Springer International Publishing

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Abstract

Person re-identification aims to match the same pedestrians across different camera views and has been applied to many important applications such as intelligent video surveillance. Due to the spatiotemporal uncertainty and visual ambiguity of pedestrian image pairs, person re-identification remains a difficult and challenging problem. The huge success of deep learning has focused attention on the use of deep features for person re-identification. However, for person re-identification, most deep learning methods minimize cross-entropy or triplet-based losses, thereby neglecting the fact that the similarities and differences between image pairs can be considered simultaneously to increase discrimination. In this paper, we propose a novel deep learning method called deep similarity feature learning (DSFL) to extract more effective deep features for image pairs. Extensive experiments on two representative person re-identification datasets (CUHK-03 and GRID) demonstrate the effectiveness and robustness of DSFL.

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Vorheriges Kapitel Facial Expression Recognition with Multi-scale Convolution Neural Network
Nächstes Kapitel Object Detection Based on Scene Understanding and Enhanced Proposals
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Metadaten
Titel
Deep Similarity Feature Learning for Person Re-identification
verfasst von
Yanan Guo
Dapeng Tao
Jun Yu
Yaotang Li
Copyright-Jahr
2016
Buch
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48889-9

Electronic ISBN: 978-3-319-48890-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48890-5_38

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