Chen, R., Jalal, M.D.A., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2018) Learning capsules for vehicle logo recognition. In: 2018 21st International Conference on Information Fusion (FUSION). 2018 21st International Conference on Information Fusion (FUSION), 10-13 Jul 2018, Cambridge, UK. IEEE , UK , pp. 565-572. ISBN 978-0-9964527-6-2
Abstract
Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches use automatically learned features from Convolutional Neural Networks (CNNs). However, CNNs do not perform well when images are rotated and very noisy. This paper proposes an image recognition framework with a capsule network. A capsule is a group of neurons, whose length can represent the existence probability of an entity or part of an entity. The orientation of a capsule contains information about the instantiation parameters such as positions and orientations. Capsules are learned by a routing process, which is more effective than the pooling process in CNNs. This paper, for the first time, develops a capsule learning framework in the field of intelligent transportation systems. By testing with the largest publicly available vehicle logo dataset, the proposed framework gives a quick solution and achieves the highest accuracy (100%) on this dataset. The learning capsules have been tested with different image changes such as rotation and occlusion. Image degradations including blurring and noise effects are also considered, and the proposed framework has proven to be superior to CNNs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 ISIF. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Intelligent Transportation Systems; Vehicle Logo Recognition; Convolutional Neural Network; Capsule Network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 688082 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Sep 2018 11:03 |
Last Modified: | 26 Sep 2018 08:08 |
Published Version: | https://doi.org/10.23919/ICIF.2018.8455227 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2018.8455227 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136086 |