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2019 | OriginalPaper | Buchkapitel

Video-Based Convolutional Attention for Person Re-Identification

verfasst von : Marco Zamprogno, Marco Passon, Niki Martinel, Giuseppe Serra, Giuseppe Lancioni, Christian Micheloni, Carlo Tasso, Gian Luca Foresti

Erschienen in: Image Analysis and Processing – ICIAP 2019

Verlag: Springer International Publishing

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Abstract

In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras. We propose a Siamese framework in which video frames of the person to re-identify and of the candidate one are processed by two identical networks which produce a similarity score. We introduce an attention mechanisms to capture the relevant information both at frame level (spatial information) and at video level (temporal information given by the importance of a specific frame within the sequence). One of the novelties of our approach is given by a joint concurrent processing of both frame and video levels, providing in such a way a very simple architecture. Despite this fact, out approach achieves better performance than the state-of-the-art on the challenging iLIDS-VID dataset.

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Metadaten
Titel
Video-Based Convolutional Attention for Person Re-Identification
verfasst von
Marco Zamprogno
Marco Passon
Niki Martinel
Giuseppe Serra
Giuseppe Lancioni
Christian Micheloni
Carlo Tasso
Gian Luca Foresti
Copyright-Jahr
2019
Buch
Image Analysis and Processing – ICIAP 2019

Print ISBN: 978-3-030-30641-0

Electronic ISBN: 978-3-030-30642-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-30642-7_1

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