AI-Powered Incident Detection for Smart City Surveillance Using Computer Vision and Automated Emergency Response

👥 Bhavya Kaushik, Archit Kumar, Shreyash Bhardwaj, Dr.Anju Saini

📙 Abstract : Because of rapid urbanization, today the surveillance systems have become inefficient for real time emergency response. This paper presents an AI-based smart city incident detection system which processes images or videos and helps in detection of incident including the severity of the incident and nearby hospitals or police stations and immediate actions that could be taken. The system has a YOLOv8n-based detection model, rule-based severity classification, OpenStreetMap-based geolocation, PDF report generation, and automated email alerts. The system is evaluated on 9,355 samples across six incident categories, in which the system achieves the system achieves 92.1% accuracy with an average latency of 1.8 seconds. The results demonstrate its effectiveness for real-time deployment in smart city environments.

🔖 Keywords :️ Smart City; Incident Detection; Computer Vision; YOLOv8; Severity Classification; Emergency Response; Geolocation; FastAPI; OpenStreetMap; Automated Alerting.