Developer Builds EyeNet to Convert Live Campus Camera Feeds Into Structured Security Alerts

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A developer has created EyeNet, a real-time campus surveillance system designed to convert raw webcam video into structured, queryable security incidents rather than simple object-detection outputs. The system uses a multi-stage pipeline combining face recognition, YOLOv8 hazard detection, and ByteTrack object tracking to ensure alerts are only triggered after a detection persists across multiple frames, significantly reducing false positives. A priority-based event bus ensures critical threats such as guns or fire are handled before lower-severity events like uniform violations, while an anomaly scoring system assigns each alert a 0–100 urgency score. Alerts, snapshots, and metrics are stored in a SQLite database, and a Flask dashboard delivers live video, real-time alerts via Server-Sent Events, and a searchable audit trail. The project was built to address common pain points in campus security, including the inability to monitor multiple feeds simultaneously and the lack of a structured, traceable alert history.

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