Kuzin, D., Isupova, O. and Mihaylova, L. (2015) Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences. In: Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2015. Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2015, 06-08 Oct 2015, Bonn, Germany. IEEE
Abstract
Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowledge of sparsity of the original signal, but it imposes certain conditions on the design matrix. The Bayesian compressive sensing approach relaxes the limitations of the conventional approach using the probabilistic reasoning and allows to include different prior knowledge about the signal structure. This paper presents two Bayesian compressive sensing methods for autonomous object detection in a video sequence from a static camera. Their performance is compared on real datasets with the non-Bayesian greedy algorithm. It is shown that the Bayesian methods can provide more effective results than the greedy algorithm in terms of both accuracy and computational time.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 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. |
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: | 02 Feb 2016 16:08 |
Last Modified: | 19 Dec 2022 13:32 |
Published Version: | http://dx.doi.org/10.1109/SDF.2015.7347706 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/SDF.2015.7347706 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:92123 |