Detection of Shoplifting on Video Using a Hybrid Network
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 wa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/10/11/199 |
| _version_ | 1797468681130737664 |
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| author | Lyudmyla Kirichenko Tamara Radivilova Bohdan Sydorenko Sergiy Yakovlev |
| author_facet | Lyudmyla Kirichenko Tamara Radivilova Bohdan Sydorenko Sergiy Yakovlev |
| author_sort | Lyudmyla Kirichenko |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:10:51Z |
| format | Article |
| id | doaj.art-f6e5f33c6ba3429fa375bcf364f464c9 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-09T19:10:51Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-f6e5f33c6ba3429fa375bcf364f464c92023-11-24T04:14:00ZengMDPI AGComputation2079-31972022-11-01101119910.3390/computation10110199Detection of Shoplifting on Video Using a Hybrid NetworkLyudmyla Kirichenko0Tamara Radivilova1Bohdan Sydorenko2Sergiy Yakovlev3Department of Applied Mathematics, Kharkiv National University of Radio Electronics, 61166 Kharkiv, UkraineDepartment of Infocommunication Engineering, Kharkiv National University of Radio Electronics, 61166 Kharkiv, UkraineDepartment of Applied Mathematics, Kharkiv National University of Radio Electronics, 61166 Kharkiv, UkraineMathematical Modelling and Artificial Intelligence Department, National Aerospace University “Kharkiv Aviation Institute”, 61072 Kharkiv, UkraineShoplifting 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.https://www.mdpi.com/2079-3197/10/11/199human behaviorshopliftingvideo surveillanceclassificationfeaturesneural network |
| spellingShingle | Lyudmyla Kirichenko Tamara Radivilova Bohdan Sydorenko Sergiy Yakovlev Detection of Shoplifting on Video Using a Hybrid Network Computation human behavior shoplifting video surveillance classification features neural network |
| title | Detection of Shoplifting on Video Using a Hybrid Network |
| title_full | Detection of Shoplifting on Video Using a Hybrid Network |
| title_fullStr | Detection of Shoplifting on Video Using a Hybrid Network |
| title_full_unstemmed | Detection of Shoplifting on Video Using a Hybrid Network |
| title_short | Detection of Shoplifting on Video Using a Hybrid Network |
| title_sort | detection of shoplifting on video using a hybrid network |
| topic | human behavior shoplifting video surveillance classification features neural network |
| url | https://www.mdpi.com/2079-3197/10/11/199 |
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