Smart Campus Surveillance System: A Multi-Modal AI Approach to Real-Time Threat Detection and Response
TL;DR
The proposed framework unifies the fight and weapon recognition, face identification and contextual interpretation of events into a unified monitoring pipeline, and a novel contribution of this work is the tool calling that allows the VLM to automatically seize important frames and trigger alert protocols in such a way that it will reduce man-in, and response delays.
Abstract
: This study presents the next generation of an intelligent surveillance system for smart campuses based on vision-language models (VLMs) for real-time multimodal detection of threat. The proposed framework unifies the fight and weapon recognition, face identification and contextual interpretation of events into a unified monitoring pipeline. A novel contribution of this work is the tool calling that allows the VLM to automatically seize important frames and trigger alert protocols in such a way that it will reduce man-in, and response delays. The system is deployed on edge devices to balance between computational efficiency and real-time performance and then a centralized surveillance dashboard is used to provide actionable insights by consolidating all the alerts and detections in the surveillance system. Preliminary evaluations show high detection accuracy and low latency, which adds to the prospects of using VLM-driven surveillance in educational environments. Beyond the technical validation, the paper discusses ethical challenges, hardware limitations, and pathways for the easy deployment on a scalable basis ultimately aligning with the SDG 16. This research contributes to the development of proactive and autonomous safety mechanisms by integrating the computer vision, language-based reasoning, and edge AI technologies in an integrated surveillance architecture.