The increasing need for security in both public and private spaces has led to the rapid adoption of surveillance technologies. Traditional surveillance systems, however, often suffer from limitations such as high bandwidth consumption, storage constraints, delayed human response, and an inability to effectively distinguish between normal and suspicious activities. This research proposes a remote surveillance system integrated with Artificial Intelligence (AI) optimization techniques to enhance efficiency, accuracy, and real-time decision-making.

The system employs intelligent video analytics powered by machine learning and computer vision algorithms to detect motion, recognize objects, and classify abnormal behaviors. Optimization techniques are incorporated to minimize false alarms, improves energy efficiency in resource-constrained environments, and ensures optimal camera placement and data transmission. Furthermore, cloud and edge computing frameworks are integrated to enable real-time monitoring from remote locations with minimal latency.

The proposed system not only automates threat detection but also optimizes resource utilization, making it scalable and adaptable for applications in smart homes, industries, and urban surveillance. Experimental evaluations demonstrate that AI-optimized surveillance significantly improves detection accuracy, reduces computational overhead, and enhances response time compared to conventional monitoring systems.