Realtime Pedestrian Recognition Using Siamese Network

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Date
2018
ORCID
Advisor
Referee
Mark
Journal Title
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Volume Title
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract
Image similarity measuring has many various applications. Pedestrian recognition is one of them and for the security purposes it is basically required to run in real-time. This paper proposes a deep Siamese neural network architecture for pedestrian recognition that achieves 70.28% accuracy on the test set containing 20 persons. Prediction of the model is fast enough for real-time processing.
Description
Citation
Proceedings of the 24th Conference STUDENT EEICT 2018. s. 441-445. ISBN 978-80-214-5614-3
http://www.feec.vutbr.cz/EEICT/
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Peer-reviewed
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Published version
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Language of document
en
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Date of acceptance
Defence
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
DOI
Citace PRO