Jaramillo-Avila, U., Aitken, J.M. orcid.org/0000-0003-4204-4020, Gurney, K. orcid.org/0000-0003-4771-728X et al. (1 more author) (2021) Robust top-down and bottom-up visual saliency for mobile robots using bio-inspired design principles. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IROS 2021 : 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 27 Sep - 01 Oct 2021, Prague, Czech Republic. Institute of Electrical and Electronics Engineers , pp. 8444-8449. ISBN 9781665417150
Abstract
Modern camera systems in robotics tend to produce overwhelming amounts of visual information due to their high resolutions and high frame rates. This raises a fundamental question of how robots should focus attention on a region of the visual scene, and how they should process information in the periphery. This is particularly an issue for mobile robots, where the computational resources of low-power embedded computing boards tend to be much less than for workstations. In this paper, we look to biological design in the primate brain for inspiration on how to solve this problem. We develop a novel computational fusion of bottom-up and top-down visual saliency information. The bottom-up saliency is produced using standard colour, intensity, and motion image processing methods. The top-down saliency is produced using a deep convolutional neural network for object detection and recognition, with foveated images for computational efficiency. Regions of attention are obtained using a computational model of the basal ganglia, thought to be involved in optimal decision making. The model of the basal ganglia is based on the multi-hypothesis sequential probability ratio test (MSPRT). The visual saliency scheme is evaluated on omnidirectional video feed highlighting a proximity to human behaviour.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 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) The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2022 09:53 |
Last Modified: | 16 Dec 2022 01:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/iros51168.2021.9636800 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187006 |