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JCR Impact Factor: 0.650
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2015 - 6

A Fisher Kernel Approach for Multiple Instance Based Object Retrieval in Video Surveillance

MIRONICA, I. See more information about MIRONICA, I. on SCOPUS See more information about MIRONICA, I. on IEEExplore See more information about MIRONICA, I. on Web of Science, MITREA, C. A. See more information about  MITREA, C. A. on SCOPUS See more information about  MITREA, C. A. on SCOPUS See more information about MITREA, C. A. on Web of Science, IONESCU, B. See more information about  IONESCU, B. on SCOPUS See more information about  IONESCU, B. on SCOPUS See more information about IONESCU, B. on Web of Science, LAMBERT, P. See more information about LAMBERT, P. on SCOPUS See more information about LAMBERT, P. on SCOPUS See more information about LAMBERT, P. on Web of Science
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Download PDF pdficon (1,590 KB) | Citation | Downloads: 348 | Views: 1,973

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

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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.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 57,338 TCR
SCOPUS® Citations for all references: 82,352 TCR

Web of Science® Average Citations per reference: 1,365 ACR
SCOPUS® Average Citations per reference: 1,961 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-01-21 13:04 in 227 seconds.

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