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Human re-identification with global and local siamese convolution neural network

Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based appr...

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Main Authors: Low, K. B., Sheikh, U. U.
Format: Article
Language:English
Published: Universitas Ahmad Dahlan 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/75636/1/KBLow_HumanRe-identificationwithGlobal.pdf
http://eprints.utm.my/id/eprint/75636/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020178250&doi=10.12928%2fTELKOMNIKA.v15i2.6121&partnerID=40&md5=0e48ee9c286279745910c7cede51fc58
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spelling my.utm.756362018-04-27T01:39:15Z http://eprints.utm.my/id/eprint/75636/ Human re-identification with global and local siamese convolution neural network Low, K. B. Sheikh, U. U. TK Electrical engineering. Electronics Nuclear engineering Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification tasks in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches. Universitas Ahmad Dahlan 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/75636/1/KBLow_HumanRe-identificationwithGlobal.pdf Low, K. B. and Sheikh, U. U. (2017) Human re-identification with global and local siamese convolution neural network. Telkomnika (Telecommunication Computing Electronics and Control), 15 (2). pp. 726-732. ISSN 1693-6930 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020178250&doi=10.12928%2fTELKOMNIKA.v15i2.6121&partnerID=40&md5=0e48ee9c286279745910c7cede51fc58
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Low, K. B.
Sheikh, U. U.
Human re-identification with global and local siamese convolution neural network
description Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification tasks in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches.
format Article
author Low, K. B.
Sheikh, U. U.
author_facet Low, K. B.
Sheikh, U. U.
author_sort Low, K. B.
title Human re-identification with global and local siamese convolution neural network
title_short Human re-identification with global and local siamese convolution neural network
title_full Human re-identification with global and local siamese convolution neural network
title_fullStr Human re-identification with global and local siamese convolution neural network
title_full_unstemmed Human re-identification with global and local siamese convolution neural network
title_sort human re-identification with global and local siamese convolution neural network
publisher Universitas Ahmad Dahlan
publishDate 2017
url http://eprints.utm.my/id/eprint/75636/1/KBLow_HumanRe-identificationwithGlobal.pdf
http://eprints.utm.my/id/eprint/75636/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020178250&doi=10.12928%2fTELKOMNIKA.v15i2.6121&partnerID=40&md5=0e48ee9c286279745910c7cede51fc58
_version_ 1643657118508646400
score 13.211869

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