Abstract:

The growing demand for enhanced security has accelerated the implementation of AI-powered threat detection in modern surveillance systems. Traditional methods that rely on manual monitoring often fall short in identifying complex and evolving threats in real time. This study introduces a comprehensive real-time data processing framework specifically designed for automated and optimized threat identification, classification, and response in surveillance applications. The proposed framework integrates advanced AI techniques, including machine learning and deep learning, to analyze large-scale data from sources such as video streams, audio inputs, and environmental sensors. Core capabilities include object detection, facial recognition, and anomaly detection, supported by real-time stream processing platforms like Apache Kafka and Apache Flink. To minimize latency and ensure faster decision-making, edge computing is incorporated near the data sources. The framework is scalable and secure, utilizing encryption, identity and access management, and adherence to data privacy regulations. Continuous model training ensures adaptability to emerging threats and improves detection accuracy over time. Case studies from urban surveillance, infrastructure protection, and law enforcement demonstrate the practical impact of the approach. By combining real-time analytics with AI, this framework offers a resilient and intelligent solution for next-generation surveillance systems, contributing significantly to the evolving landscape of AI-enhanced security.

Description:

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