Jaramillo-Avila, U., Aitken, J.M. orcid.org/0000-0003-4204-4020 and Anderson, S.R. orcid.org/0000-0002-7452-5681 (2020) Visual saliency with foveated images for fast object detection and recognition in mobile robots using low-power embedded GPUs. In: Proceedings of the 19th International Conference on Advanced Robotics (ICAR). 2019 19th International Conference on Advanced Robotics (ICAR), 02-06 Dec 2019, Belo Horizonte, Brazil. IEEE , pp. 773-778. ISBN 9781728124681
Abstract
This paper presents a visual saliency algorithm for fast object detection and recognition in mobile robots using low power graphics processing units (GPUs), based on human vision foveation. The step of image foveation enables the use of small images, which leads to a much reduced number of computations in deep convolutional neural networks and consequent increase in frame-rate. We demonstrate how using a simple foveated downsampling method, we can maintain a detection-recognition performance level similar to the level at larger image resolutions, even when transforming from 416x416 to 128x128 pixels, for a small high acuity region of the image, which can lead to a 4× speed up in frame rates, maintaining a relatively stable mean Average Precision. The visual saliency algorithm is evaluated on the Stanford drone dataset and our own experimental drone dataset.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. 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. |
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 DREAM4CARS 731593 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Oct 2019 12:59 |
Last Modified: | 06 Feb 2021 01:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICAR46387.2019.8981557 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152364 |