From Cloud to Camera: Transitioning AI Video Analytics to the Edge in Next-Gen Surveil

Abstract

            The rapid evolution of artificial intelligence (AI) has transformed modern video surveillance systems, enabling real-time object detection, behavior analysis, and anomaly recognition. Traditionally, these intelligent functions have relied heavily on cloud-based processing, which introduces challenges such as latency, bandwidth limitations, and privacy concerns. This article explores the shift toward edge computing in surveillance networks—where AI inference occurs directly on or near the camera device. By analyzing the architectural, technical, and operational differences between cloud-based and edge-based systems, the paper highlights the advantages of edge AI in enabling low-latency, scalable, and privacy-preserving surveillance solutions. The discussion also addresses deployment challenges, hardware considerations, and future directions in edge-based video analytics.