Aftab, W., De Freitas, A., Arvaneh, M. et al. (1 more author) (2017) A Gaussian Process Approach for Extended Object Tracking with Random Shapes and for Dealing with Intractable Likelihoods. In: Proceedings of the 2017 International Conference on Digital Signal Processing (DSP). 2017 22nd International Conference on on Digital Signal Processing (DSP 2017), 23-25 Aug 2017, London, UK. IEEE
Abstract
Tracking of arbitrarily shaped extended objects is a complex task due to the intractable analytical expression of measurement to object associations. The presence of sensor noise and clutter worsens the situation. Although a significant work has been done on the extended object tracking (EOT) problems, most of the developed methods are restricted by assumptions on the shape of the object such as stick, circle, or other axis-symmetric properties etc. This paper proposes a novel Gaussian process approach for tracking an extended object using a convolution particle filter (CPF). The new approach is shown to track irregularly shaped objects efficiently in presence of measurement noise and clutter. The mean recall and precision values for the shape, calculated by the proposed method on simulated data are around 0.9, respectively, by using 1000 particles.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 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. |
Keywords: | Gaussian process approach; random shapes; intractable likelihoods; arbitrarily shaped extended objects; object associations; sensor noise; extended object tracking problems; axis-symmetric properties; measurement noise; convolution particle filter |
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 - FP6/FP7 TRAX - 607400 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jul 2017 08:43 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.1109/ICDSP.2017.8096087 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICDSP.2017.8096087 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118899 |