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.
| Original language | English |
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| Title of host publication | 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF) |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-4673-7175-9 |
| DOIs | |
| Publication status | Published - 17 Dec 2015 |
| Event | Workshop on Sensor Data Fusion, SDF 2015 - Bonn, Germany Duration: 6 Oct 2015 → 8 Oct 2015 |
Publication series
| Name | 2015 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2015 |
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Conference
| Conference | Workshop on Sensor Data Fusion, SDF 2015 |
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| Country/Territory | Germany |
| City | Bonn |
| Period | 6/10/15 → 8/10/15 |
Funding
Acknowledgements: The authors Olga Isupova and Lyudmila Mihaylova are grateful for the support provided by the EC Seventh Framework Programme [FP7 2013-2017] TRAcking in compleX sensor systems (TRAX) Grant agreement no.: 607400. Lyudmila Mihaylova acknowledges also the support from the UK Engineering and Physical Sciences Research Council (EPSRC) via the Bayesian Tracking and Reasoning over Time (BTaRoT) grant EP/K021516/1.