ViEdge: Video Analytics on Distributed Edge

With the development of edge computing and increasing demand on video analytics, it is attractive to implement distributed video analytics across edge devices. In this article, we propose ViEdge, a distributed video analytics system across edge devices. ViEdge differs from status quo edge-side video analytics systems in: First, ViEdge does not assume the existence of an edge/cloud server. Instead of processing in a cascaded way between edge devices and server, the video analytics in ViEdge is processed across distributed edge devices in a parallel way. Second, ViEdge addresses two practical challenges in video analytics systems. Specifically, ViEdge optimizes the performance of glance-and-focus object detection pipeline and query related processing with multiple query types. The characters of edge devices (computing capabilities and network conditions) and features of query types (computational complexities and input/output sizes) are comprehensively considered in development of components in ViEdge. By modeling the challenges as multiway number partitioning problems, ViEdge provides practical solutions to optimizing the object detection pipeline and allocations of multiple queries of different types across distributed edge devices. Compared to baseline methods in distributed video analytics across edge devices, ViEdge reaches 1.4× to 5.3× speedup in different network environments with neglectable overhead.