{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:30:14Z","timestamp":1775745014272,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,6]],"date-time":"2022-11-06T00:00:00Z","timestamp":1667692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beethoven","award":["DFG-NCN 2016\/23\/G\/ST1\/04083"],"award-info":[{"award-number":["DFG-NCN 2016\/23\/G\/ST1\/04083"]}]},{"name":"the Ministry of Education and Science of Ukraine \u201cTechnologies, tools for mathematical modeling, optimization and system analysis of coverage problems in space monitoring systems\u201d","award":["DFG-NCN 2016\/23\/G\/ST1\/04083"],"award-info":[{"award-number":["DFG-NCN 2016\/23\/G\/ST1\/04083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Shoplifting is a major problem for shop owners and many other parties, including the police. Video surveillance generates huge amounts of information that staff cannot process in real time. In this article, the problem of detecting shoplifting in video records was solved using a classifier, which was a hybrid neural network. The hybrid neural network included convolutional and recurrent ones. The convolutional network was used to extract features from the video frames. The recurrent network processed the time sequence of the video frames features and classified the video fragments. In this work, gated recurrent units were selected as the recurrent network. The well-known UCF-Crime dataset was used to form the training and test datasets. The classification results showed a high accuracy of 93%, which was higher than the accuracy of the classifiers considered in the review. Further research will focus on the practical implementation of the proposed hybrid neural network.<\/jats:p>","DOI":"10.3390\/computation10110199","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T02:43:51Z","timestamp":1667789031000},"page":"199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Detection of Shoplifting on Video Using a Hybrid Network"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2780-7993","authenticated-orcid":false,"given":"Lyudmyla","family":"Kirichenko","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine"},{"name":"Applied Mathematics Department, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5975-0269","authenticated-orcid":false,"given":"Tamara","family":"Radivilova","sequence":"additional","affiliation":[{"name":"Department of Infocommunication Engineering, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine"}]},{"given":"Bohdan","family":"Sydorenko","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine"}]},{"given":"Sergiy","family":"Yakovlev","sequence":"additional","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 61072 Kharkiv, Ukraine"},{"name":"Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"ref_1","unstructured":"Chemere, D.S. 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