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Learning temporal information from spatial information using CAPSNETS for human action recognition
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Algamdi, Abdullah M., Sanchez Silva, Victor and Li, Chang-Tsun (2019) Learning temporal information from spatial information using CAPSNETS for human action recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12-17 May 2019 ISBN 9781479981311. ISSN 2379-190X. doi:10.1109/ICASSP.2019.8683720
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WRAP-learning-temporal-information-spatial-information-using-CAPSNETS-human-action-recognition-Li-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1030Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ICASSP.2019.8683720
Abstract
Capsule Networks (CapsNets) are recently introduced to overcome some of the shortcomings of traditional Convolutional Neural Networks (CNNs). CapsNets replace neurons in CNNs with vectors to retain spatial relationships among the features. In this paper, we propose a CapsNet architecture that employs individual video frames for human action recognition without explicitly extracting motion information. We also propose weight pooling to reduce the computational complexity and improve the classification accuracy by appropriately removing some of the extracted features. We show how the capsules of the proposed architecture can encode temporal information by using the spatial features extracted from several video frames. Compared with a traditional CNN of the same complexity, the proposed CapsNet improves action recognition performance by 12.11% and 22.29% on the KTH and UCF-sports datasets, respectively.
| Item Type: | Conference Item (Paper) | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Optical pattern recognition, Image processing -- Digital techniques, Computer vision, Computer vision -- Mathematical models, Pattern perception, Neural networks (Computer science) , Human face recognition (Computer science) | ||||||||
| Publisher: | IEEE | ||||||||
| ISBN: | 9781479981311 | ||||||||
| ISSN: | 2379-190X | ||||||||
| Official Date: | 2019 | ||||||||
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| DOI: | 10.1109/ICASSP.2019.8683720 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Publisher Statement: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||||
| Title of Event: | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | ||||||||
| Type of Event: | Conference | ||||||||
| Location of Event: | Brighton, UK | ||||||||
| Date(s) of Event: | 12-17 May 2019 |
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