mirtrМир транспортаWorld of Transport and Transportation1992-3252Russian University of Transport (RUT)10.30932/1992-3252-2017-15-6-18mirtr-1388Research ArticleБЕЗОПАСНОСТЬSAFETY AND SECURITYИнтеллектуальный видеоанализ опасных ситуацийINTELLIGENT VIDEO ANALYSIS OF DANGEROUS SITUATIONSАнищенкоЛ. Н.AnishchenkoL. N.anishchenko@rslab.ruИвашовС. И.IvashovS. I.sivashiv@rslab.ruСкребковА. В.SkrebkovA. V.skrebkov_av@mail.ruМГТУ им. Н. Э. БауманаRussian FederationРоссийский университет транспорта (МИИТ)Russian Federation201728122017156182193Copyright © Анищенко Л.Н., Ивашов С.И., Скребков А.В., 20172017Анищенко Л.Н., Ивашов С.И., Скребков А.В.Anishchenko L.N., Ivashov S.I., Skrebkov A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://mirtr.elpub.ru/jour/article/view/1388

Текст аннотации на англ. языке и полный текст статьи на англ. языке находится в прилагаемом файле ПДФ (англ. версия следует после русской версии).Работа выполнена при поддержке Российского фонда фундаментальных исследований (грант № 17-20-03034). Статья посвящена разработке системы интеллектуального анализа видеозаписей камер наружного наблюдения, позволяющей выявлять опасные ситуации на объектах железных дорог на примере детекции падений в зоне пути. Предложен метод предобработки видеоряда с целью формирования пространства признаков, основанный на использовании вычитания фона по методу гауссовой смеси, последующем отслеживании перемещения человека при помощи фильтра Калмана и деформации формы подвижного объекта в результате применения прокрустова анализа. Обоснован подбор оптимального состава пространства признаков и дополнительных эвристик, обеспечивающих выделение эпизодов падений по видеозаписи со средним качеством каппы Коэна 0,62 по сравнению с визуальным анализом оператором.

[For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version)].The work was supported by the Russian Foundation for Basic Research (Grant No. 17-20-03034). ABSTRACT The article is devoted to development of a system for the intelligent analysis of video recordings of external surveillance cameras, which makes it possible to identify dangerous situations at railway facilities using the example of detection of falls in the track area. A method of preprocessing a video for the purpose of forming a feature space based on the use of background subtraction using the Gaussian mixture method, followed by tracking the movement of a person with the help of the Kalman filter and deformation of the shape of the mobile object as a result of applying the procrustean analysis is proposed. The selection of the optimal composition of the feature space and additional heuristics providing the isolation of episodes of falls from video recording with an average quality of the Cohen’s kappa 0,62 is compared with the visual analysis by the operator. Keywords: railway, safety, video surveillance, intelligent video analysis, motion recognition, machine learning, form analysis.

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The authors declare that there are no conflicts of interest present.