Okokpujie, Kennedy O. and Sodipo, Queen B. and Akingunsoye, Adenugba Vincent and Awomoyi, Morayo E. (2023) Deep-Learning Algorithms for Video Anomaly Detection: A Mini-Review. In: 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), 01-03 November 2023, Abuja, Nigeria.
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Abstract
With the increasing demand for security in the day-to-day lives of people, especially when in public spaces, surveillance system helps to monitor human behavior, prevent illegal activities and detect anomalous events. Anomaly detection in video involves the process of detecting anomalous behaviors that occur in such video. Due to the inefficiency of humans when it comes to surveillance, various algorithms can help to automatically detect these anomalous events in videos. Deep learning is fast rising, which is very useful as it imitates how humans gain knowledge and this helps in the area of surveillance and detection. This study aims to provide a comprehensive review of deep learning algorithms used for the detection of anomalous events captured in videos. This study also captures the datasets available for anomaly detection, the applications and future research directions
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science Faculty of Engineering, Science and Mathematics > School of Mathematics |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 27 Nov 2024 15:06 |
| Last Modified: | 27 Nov 2024 15:06 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18616 |
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