AI-Driven Real-Time Surveillance: Anomaly Detection and Notification System

Surveillance technologies are rapidly transitioning from traditional passive monitoring systems to intelligent platforms capable of detecting anomalies in real-time. This study presents an AI-powered surveillance framework designed to identify violent behavior across diverse input formats, such as static images, recorded videos, and live webcam feeds. Leveraging advanced deep learning architectures, including YOLO and TensorFlow-based models, the system delivers high-precision violence detection with minimal latency. On identifying a threat, it initiates instant alerts through visual prompts and sound notifications, ensuring rapid situational awareness. The interface is intuitively built to support real-time configuration of detection parameters and the monitoring of performance indicators such as detection frequency, operational uptime, and frame processing speed. Engineered for scalability and resilience, the system demonstrates strong applicability in domains like public safety, institutional monitoring, and content regulation. Its core advantages include high detection accuracy, efficient real-time processing, and adaptability to various data sources. By combining cutting edge AI strategies with responsive caution instruments, the framework offers a reliable, computerized arrangement for improving danger location in observation situations.