HYBRID AI FRAMEWORK FOR EFFICIENT ANOMALY DETECTION IN VIDEO SURVEILLANCE DATA

Автор(и)

DOI:

https://doi.org/10.30888/2663-5712.2025-33-01-083

Ключові слова:

artificial intelligence, video surveillance, information systems, genetic algorithm, modeling, computer vision, video surveillance data

Анотація

Contemporary video surveillance infrastructure produces substantial data streams, posing challenges for efficient real-time processing. Current automated anomaly detection techniques frequently demand extensive computational resources and function as opaq

Посилання

Salman, M., Abbas, N., Rahman, S. I. U., Rehman, A., Alamri, F. S., Elyassih, A., & Saba, T. (2025). Enhancing surveillance anomaly detection with keyframes and explainable inception model. Egyptian Informatics Journal, 31, 100769. https://doi.org/10.1016/j.eij.2025.100769

Masud U, Sadiq M, Masood S, Ahmad M, Abd El-Latif AA. LW-DeepFakeNet: a lightweight time distributed CNN-LSTM network for real-time DeepFake video detection. SIViP 2023;17(8):4029–37.

Theobald O. Machine learning: make your recommender system; build your recommender system with machine learning insights. Packt Publishing Ltd 2024

Bensakhria, Ayoub. “Leveraging Real-time Edge AI-Video Analytics to Detect and Prevent Threats in Sensitive Environments.” (2023).

Skladannyi, P., Kostiuk, Y., Rzaieva, S., Bebeshko, B., & Korshun, N. (2025). Adaptive Methods for Embedding Digital Watermarks to Protect Audio and Video Images in Information Systems. https://ceur-ws.org/Vol-4016/

Bondarchuk, A., Dibrivniy, O., Grebenyk, V., & Onyshchenko, V. (2021). Motion Vector Search Algorithm for Motion Compensation in Video Encoding. In 2021 IEEE 8th International Conference on Problems of Infocommunications, Science and Technology (pp. 345-348). IEEE. doi: 10.1109/PICST54195.2021.9772109

Buinytska, O., & Smirnova, V. (2024). Artificial intelligence technologies in research activities: overview and application. Continuing Professional Education: Theory and Practice, 81(4), 31–46. https://doi.org/10.28925/2412-0774.2024.4.2

Chemerys, O., Bushma, O., Lytvyn, O., Belotserkovsky, A., & Lukashevich, P. (2021). Network of Autonomous Units for the Complex Technological Objects Reliable Monitoring. In Reliability Engineering and Computational Intelligence (pp. 261-274). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-74556-1_16

Zhurakovskyi, B., Poltorak, V., Toliupa, S., Pliushch, O., & Platonenko, A. (2024). Processing and Analyzing Images based on a Neural Network. https://ceur-ws.org/Vol-3654/paper11.pdf

Опубліковано

2025-09-30

Як цитувати

Бондарчук, А., Бушма, О., Довженко, Т., & Гашко, А. (2025). HYBRID AI FRAMEWORK FOR EFFICIENT ANOMALY DETECTION IN VIDEO SURVEILLANCE DATA. SWorldJournal, 1(33-01), 188–195. https://doi.org/10.30888/2663-5712.2025-33-01-083

Номер

Розділ

Статті