Visual data produced at the edge is rich with information, opening a world of analytics opportunities for applications to explore. However, the demanding requirements of visual data on computational resources and bandwidth have hindered effective processing, preventing the data from being used in an economically efficient manner. In order to scale out visual analytics systems, it is necessary to have a framework that works collaboratively between edge and cloud. In this paper, we propose an end-to-end (E2E) visual fog architecture, designed for processing and management of visual data. Using our architecture to extract shopper insights, we are able to achieve application specified real time requirements for extracting and querying visual data, showing the feasibility of our design in a real-world setting. We also discuss the lessons we learned from deploying an edge-to-cloud architecture for video streaming applications.
