Aftab, W., De Freitas, A., Arvaneh, M. et al. (1 more author) (2018) A Gaussian Process Convolution Particle Filter for Multiple Extended Objects Tracking with Non-Regular Shapes. In: 2018 21st International Conference on Information Fusion (FUSION) (FUSION 2018). 21st International Conference on Information Fusion, 10-13 Jul 2018, Cambridge, UK. IEEE ISBN 978-0-9964527-6-2
Abstract
Extended object tracking has become an integral part of various autonomous systems in diverse fields. Although it has been extensively studied over the past decade, many complex challenges remain in the context of extended object tracking. In this paper, a new method for tracking multiple irregularly shaped extended objects using surface measurements is proposed. The Gaussian Process Convolution Particle Filter proposed in [1], designed to track a single extended/group object, is enhanced for tracking multiple extended objects. A convolution kernel is proposed to estimate the multi-object likelihood. A target birth/death model based on the proposed method is also introduced for automatic initiation and deletion of the objects. The proposed approach is validated on real-world LiDAR data which shows that the method is efficient in tracking multiple irregularly shaped extended objects in challenging scenarios involving occlusion, dense clutter and low object detection.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Jun 2018 08:38 |
Last Modified: | 19 Dec 2022 13:49 |
Published Version: | https://doi.org/10.23919/ICIF.2018.8455501 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2018.8455501 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:131615 |