Abstract
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.
Funder
Beethoven
the Ministry of Education and Science of Ukraine “Technologies, tools for mathematical modeling, optimization and system analysis of coverage problems in space monitoring systems”
Subject
Applied Mathematics,Modeling and Simulation,General Computer Science,Theoretical Computer Science
Reference35 articles.
1. Chemere, D.S. (2018). Real-time Shoplifting Detection from Surveillance Video. [Master Thesis, Addis Ababa University].
2. Kirichenko, L., and Radivilova, T. (2017, January 21–25). Analyzes of the distributed system load with multifractal input data flows. Proceedings of the 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics, CADSM 2017, Lviv, Ukraine.
3. An Automatic Shoplifting Detection from Surveillance Videos;Proceedings of the AAAI Conference on Artificial Intelligence,2020
4. Ivanisenko, I., Kirichenko, L., and Radivilova, T. (2016, January 23–27). Investigation of multifractal properties of additive data stream. Proceedings of the 2016 IEEE 1st International Conference on Data Stream Mining and Processing, Lviv, Ukraine.
5. Kirichenko, L., Radivilova, T., and Bulakh, V. (2019). Machine learning in classification time series with fractal properties. Data, 4.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献