The Library
EV-Gait : Event-based robust gait recognition using dynamic vision sensors
Tools
Tools
Tools
Wang, Yanxiang, Du, Bowen, Shen, Yiran, Wu, Kai, Zhao, Guangrong, Sun, Jianguo and Wen, Hongkai (2020) EV-Gait : Event-based robust gait recognition using dynamic vision sensors. In: 2019 IEEE International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 15-20 Jun 2019. Published in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ISBN 9781728132945. doi:10.1109/CVPR.2019.00652 ISSN 2575-7075.
![[img]](https://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-EV-Gait-event-robust-recognition-dynamic-sensors-Wen-2019.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (2548Kb) |
Official URL: https://doi.org/10.1109/CVPR.2019.00652
Abstract
In this paper, we introduce a new type of sensing modality, the Dynamic Vision Sensors (Event Cameras), for the task of gait recognition. 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 rather than frames, where conventional vision-based gait recognition algorithms can't be directly applied. To address this, we propose a new Event-based Gait Recognition (EV-Gait) approach, which exploits motion consistency to effectively remove noise, and uses a deep neural network to recognise gait from the event streams. To evaluate the performance of EV-Gait, we collect two event-based gait datasets, one from real-world experiments and the other by converting the publicly available RGB gait recognition benchmark CASIA-B. Extensive experiments show that EV-Gait can get nearly 96% recognition accuracy in the real-world settings, while on the CASIA-B benchmark it achieves comparable performance with state-of-the-art RGB-based gait recognition approaches.
| Item Type: | Conference Item (Paper) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Computer vision, Pattern recognition systems, Image processing -- Digital techniques, Depth perception, Gait in humans -- Computer programs | ||||||||
| Journal or Publication Title: | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | ||||||||
| Publisher: | IEEE | ||||||||
| ISBN: | 9781728132945 | ||||||||
| ISSN: | 2575-7075 | ||||||||
| Official Date: | 9 January 2020 | ||||||||
| Dates: |
|
||||||||
| DOI: | 10.1109/CVPR.2019.00652 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 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: | 6 March 2019 | ||||||||
| Conference Paper Type: | Paper | ||||||||
| Title of Event: | 2019 IEEE International Conference on Computer Vision and Pattern Recognition | ||||||||
| Type of Event: | Conference | ||||||||
| Location of Event: | Long Beach, CA | ||||||||
| Date(s) of Event: | 15-20 Jun 2019 | ||||||||
| Related URLs: | |||||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
