Enhancing the Intelligence of Real-Time Video Surveillance Systems with Automated Anomaly Detection and Response

This study addresses the critical need for advanced threat detection systems by enhancing real-time video surveillance with automated anomaly detection and response. It integrates machine learning algorithms for object and activity recognition, leveraging AI and parallel computing for efficient and accurate anomaly detection. The research evaluates the adaptability and effectiveness of machine learning models in diverse environments, emphasizing robust response protocols for identified anomalies. Key findings highlight challenges in identifying suspects amidst distractions like vehicles, animals, and crowds. Advanced preprocessing and machine learning techniques significantly improved detection accuracy from 70.4% to 93%. Parallel computing reduced model training time from 71 minutes to just 8 minutes, showcasing its efficiency. The study recommends cost-effective solutions like building high-performance computing (HPC) clusters using Raspberry Pi nodes for training and employing Hadoop clusters for video stream processing. Deploying optimized models on scalable web servers enables real-time diagnostics and anomaly alerts, empowering security operators with faster and more actionable insights. These contributions advance the development of intelligent, efficient, and scalable surveillance systems that address modern security challenges. The proposed methods improve decision-making and real-time responsiveness, providing a significant leap forward in video surveillance technology.