A Proposed Framework for a Multi-Scenario Intelligent Surveillance System in Educational Institutions

The reliance on manual CCTV monitoring in educational institutions leaves critical vulnerabilities in both physical safety and academic fairness. This paper proposed an automated, multi-scenario surveillance system that integrates advanced computer vision and deep learning techniques to address these flaws. By synthesizing YOLOv7 and MobileNet-BiLSTM for rapid violence detection alongside an MLP-enhanced YOLOv8 and ResNet 3D CNN for precise cheating recognition, the framework provides a comprehensive solution optimized for edge deployment. Future iterations of this project will focus on the empirical implementation of these algorithms, testing them against custom datasets, and integrating the analytical backend with a real-time web dashboard for institutional administration.

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