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Event-stream representation for human gaits identification using deep neural networks
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Wang, Yanxiang, Zhang, Xian, Shen, Yiran, Du, Bowen, Zhao, Guangrong, Cui, Lizhen and Wen , Hongkai (2022) Event-stream representation for human gaits identification using deep neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (7). pp. 3436-3449. doi:10.1109/TPAMI.2021.3054886 ISSN 0162-8828.
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WRAP-Event-stream-representation-human-gaits-2021.pdf - Accepted Version - Requires a PDF viewer. Download (7Mb) | Preview |
Official URL: https://doi.org/10.1109/TPAMI.2021.3054886
Abstract
Dynamic vision sensors (event cameras) are recently introduced to solve a number of different vision tasks such as object recognition, activities recognition, tracking, etc.Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and much larger dynamic range. However, those cameras only produce noisy and asynchronous events of intensity changes, i.e., event-streams rather than frames, where conventional computer vision algorithms can't be directly applied. We hold the opinion that the key challenge of improving the performance of event cameras in vision tasks is finding the appropriate representations of the event-streams so that cutting-edge learning approaches can be applied to fully uncover the spatial-temporal information contained in the event-streams. In this paper, we focus on the event-based human gait identification task and investigate the possible representations of the event-streams when deep neural networks are applied as the classifier. We propose new event-based gait Recognition approaches basing on two different representations of the event-stream, i.e., graph and image-like representations, and use Graph-based Convolutional Network (GCN) and Convolutional Neural Networks (CNN) respectively to recognize gait from the event-streams.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Gait in humans -- Computer simulation, Biometric identification, Pattern recognition systems, Human activity recognition , Computer vision, Neural networks (Computer science) | ||||||||||||
Journal or Publication Title: | IEEE Transactions on Pattern Analysis and Machine Intelligence | ||||||||||||
Publisher: | IEEE | ||||||||||||
ISSN: | 0162-8828 | ||||||||||||
Official Date: | 1 July 2022 | ||||||||||||
Dates: |
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Volume: | 44 | ||||||||||||
Number: | 7 | ||||||||||||
Page Range: | pp. 3436-3449 | ||||||||||||
DOI: | 10.1109/TPAMI.2021.3054886 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Reuse Statement (publisher, data, author rights): | © 2021 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 | ||||||||||||
Date of first compliant deposit: | 4 February 2021 | ||||||||||||
Date of first compliant Open Access: | 4 February 2021 | ||||||||||||
RIOXX Funder/Project Grant: |
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