Compressive sensing approaches for autonomous object detection in video sequences

Danil Kuzin, Olga Isupova, Lyudmila Mihaylova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publication2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-4673-7175-9
DOIs
Publication statusPublished - 17 Dec 2015
EventWorkshop on Sensor Data Fusion, SDF 2015 - Bonn, Germany
Duration: 6 Oct 20158 Oct 2015

Publication series

Name2015 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2015

Conference

ConferenceWorkshop on Sensor Data Fusion, SDF 2015
CountryGermany
CityBonn
Period6/10/158/10/15

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