期刊
IEEE INTELLIGENT SYSTEMS
卷 38, 期 1, 页码 34-44出版社
IEEE COMPUTER SOC
DOI: 10.1109/MIS.2022.3214614
关键词
Image edge detection; Servers; Collaboration; Cloud computing; Cameras; Streaming media; Computer architecture
This article discusses low-latency edge-cloud collaborative video analytic applications (ECCVApps) and presents ECCVideo, a system that supports the unified management of servers at both the edge and cloud. It provides the architecture and services necessary for the development and deployment of large-scale ECCVApps. A real-time object detection application is deployed to validate the proposed system.
Video analysis drives a wide range of applications in the fields of public safety, autonomous vehicles, etc., with the great potential to impact society. Traditional cloud-based approaches are not applicable because of prohibitive bandwidth consumption and high response latency, while simply edge-based video analysis suffers from large computation delay, considering the restricted computing capacity of edge servers. Therefore, in this article, we focus on low-latency edge-cloud collaborative video analytic applications (ECCVApps) by making full use of resources at both the edge and cloud. Particularly, we present an edge-cloud collaborative video analysis system called ECCVideo, to support the unified management of heterogeneous servers and facilitate the development and deployment of large-scale ECCVApps. Under ECCVideo, we design the application architecture of ECCVApps, including presentation paradigm, transparent communication services, and full lifecycle management. To validate the proposed system, a real-time object detection application is deployed on the ECCVideo prototype.
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