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A Fisher Kernel Approach for Multiple Instance Based Object Retrieval in Video SurveillanceMIRONICA, I.   , MITREA, C. A. , IONESCU, B. , LAMBERT, P. |
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Author keywords
automated video surveillance, Fisher kernel representation, multiple-instance object retrieval
References keywords
recognition(11), video(10), vision(9), surveillance(9), image(9), processing(7), pattern(7), machine(7), classification(7), fisher(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 43 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04006
Web of Science Accession Number: 000368499800006
SCOPUS ID: 84949964857
Abstract
This paper presents an automated surveillance system that exploits the Fisher Kernel representation in the context of multiple-instance object retrieval task. The proposed algorithm has the main purpose of tracking a list of persons in several video sources, using only few training examples. In the first step, the Fisher Kernel representation describes a set of features as the derivative with respect to the log-likelihood of the generative probability distribution that models the feature distribution. Then, we learn the generative probability distribution over all features extracted from a reduced set of relevant frames. The proposed approach shows significant improvements and we demonstrate that Fisher kernels are well suited for this task. We demonstrate the generality of our approach in terms of features by conducting an extensive evaluation with a broad range of keypoints features. Also, we evaluate our method on two standard video surveillance datasets attaining superior results comparing to state-of-the-art object recognition algorithms. |
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[1] End to end very deep person re-identification, Stefan, Liviu-Daniel, Mironica, Ionut, Mitrea, Catalin Alexandru, Ionescu, Bogdan, 2017 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-5386-0674-2, 2017.
Digital Object Identifier: 10.1109/ISSCS.2017.8034923 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
