QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collaborative System

The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditional cloud-based intelligent video processing faces significant challenges in terms of latency and bandwidth, while the pure edge computing approach is deficient in query accuracy due to its lack of computational power. Existing edge cloud collaboration approaches, such as SurveilEdge, focus on real-time target queries within a single video stream and do not show promising results during target queries across multiple video streams. For this reason, this paper proposes Query Edge, an edge-cloud collaborative real-time query system for multiple video streams. Specifically, we design a real-time query system based on an edge-cloud collaboration framework to achieve highly accurate and low-latency target query services in multiple video streams. In addition, we introduce a prioritization mechanism and a load-balancing strategy in the query task scheduling process to further improve query efficiency. The evaluation proves that QueryEdge has a significant improvement in query latency and bandwidth consumption compared with pure cloud computing, pure edge computing, and SurveilEdge.