2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 2 , PP: 77-92, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
M. E. ElAlmi 1 * , M. M. Lotfy 2 * , M. M. Ghoniem 3 *
Doi: https://doi.org/10.54216/FPA.200207
Surveillance cameras play a pivotal role in educational institutions. They monitor the educational process, detect violations, and protect students from potential injuries or dangers. Continuous recording generates a massive amount of video data. Human observers spend significant time and effort reviewing the footage. Reviewing aims to detect and quickly address abnormal events. Abnormal events are rare in educational environments. Observers may become bored during continuous monitoring. This may cause fatigue and loss of attention. To overcome these challenges, this paper proposes an intelligent system that combines summarization and abnormal event detection in surveillance video. It is divided into two stages: The first stage starts with the extraction of static, feature-based key frames that highlight the video's most significant content. In the second stage, Convolutional Autoencoder (CAE) network used to detect abnormal events from the key frames generated by the summary stage. The proposed system produces two separate videos: a general summary and a dedicated abnormal events video sent to the relevant individuals. The proposed system was tested on some benchmark datasets. The experimental results demonstrated that the proposed system was effective in reducing browsing time and effort, as well as in detecting abnormal events within an educational context.
Static summarization , Surveillance video , Convolutional Autoencoder (CAE) , Abnormal Events
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