mathematical modeling, percolation theory, probability theory, swarm of robots.

In this paper, we propose an incremental learning scheme for the abnormal behavior detection algorithm based on principal component. The results obtained on a UCSD dataset and our experimental videos at a different number of training samples show that error rates are similar to conventional learning...

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Main Authors: R.A. Shatalin, V.R. Fidelman, P.E. Ovchinnikov
Format: Article
Language:English
Published: Samara National Research University 2020-06-01
Series:Компьютерная оптика
Subjects:
Online Access:http://computeroptics.smr.ru/KO/PDF/KO44-3/440320.pdf
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author R.A. Shatalin
V.R. Fidelman
P.E. Ovchinnikov
author_facet R.A. Shatalin
V.R. Fidelman
P.E. Ovchinnikov
author_sort R.A. Shatalin
collection DOAJ
description In this paper, we propose an incremental learning scheme for the abnormal behavior detection algorithm based on principal component. The results obtained on a UCSD dataset and our experimental videos at a different number of training samples show that error rates are similar to conventional learning. Moreover, the proposed scheme allows the incremental learning time to be significantly reduced in comparison with a method based on matrix eigendecomposition.
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spelling doaj.art-ad1007e6dd294df5a624c1d816306d482022-12-21T20:37:59ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792020-06-0144347648110.18287/2412-6179-CO-624_1mathematical modeling, percolation theory, probability theory, swarm of robots.R.A. Shatalin0V.R. Fidelman1P.E. Ovchinnikov2Lobachevsky State University of Nizhny Novgorod, Nizny Novgorod, RussiaLobachevsky State University of Nizhny Novgorod, Nizny Novgorod, RussiaLobachevsky State University of Nizhny Novgorod, Nizny Novgorod, RussiaIn this paper, we propose an incremental learning scheme for the abnormal behavior detection algorithm based on principal component. The results obtained on a UCSD dataset and our experimental videos at a different number of training samples show that error rates are similar to conventional learning. Moreover, the proposed scheme allows the incremental learning time to be significantly reduced in comparison with a method based on matrix eigendecomposition.http://computeroptics.smr.ru/KO/PDF/KO44-3/440320.pdfincremental learningvideo analysisanomaly detectionprincipal component analysis
spellingShingle R.A. Shatalin
V.R. Fidelman
P.E. Ovchinnikov
mathematical modeling, percolation theory, probability theory, swarm of robots.
Компьютерная оптика
incremental learning
video analysis
anomaly detection
principal component analysis
title mathematical modeling, percolation theory, probability theory, swarm of robots.
title_full mathematical modeling, percolation theory, probability theory, swarm of robots.
title_fullStr mathematical modeling, percolation theory, probability theory, swarm of robots.
title_full_unstemmed mathematical modeling, percolation theory, probability theory, swarm of robots.
title_short mathematical modeling, percolation theory, probability theory, swarm of robots.
title_sort mathematical modeling percolation theory probability theory swarm of robots
topic incremental learning
video analysis
anomaly detection
principal component analysis
url http://computeroptics.smr.ru/KO/PDF/KO44-3/440320.pdf
work_keys_str_mv AT rashatalin mathematicalmodelingpercolationtheoryprobabilitytheoryswarmofrobots
AT vrfidelman mathematicalmodelingpercolationtheoryprobabilitytheoryswarmofrobots
AT peovchinnikov mathematicalmodelingpercolationtheoryprobabilitytheoryswarmofrobots