Enhancing Public Safety Situational Awareness Using Edge Intelligence

Real-time video analytics powered by artificial intelligence (AI) enables public safety agents to effectively perceive and respond to dynamic environments. However, processing large-scale video streams introduces computational and latency challenges. This work presents a framework that combines edge and cloud computing to facilitate efficient AI-based processing of video streams for public safety applications. We evaluated the framework’s performance in a face recognition task by comparing edge and cloud processing. Our initial results demonstrate that edge processing achieves lower total latency compared to cloud processing despite higher inference times, primarily due to reduced transmission overhead. The framework also achieves high accuracy in recognition tasks, though with trade-offs in recall.