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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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automated video surveillance, Fisher kernel representation, multiple-instance object retrieval
recognition(11), video(10), vision(9), surveillance(9), image(9), processing(7), pattern(7), machine(7), classification(7), fisher(6)
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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
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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