Distributed Middleware for Edge Vision Systems

Recent advances in Deep Learning, have made possible distributed multi-camera vision analytics targeted at a variety of surveillance applications involving automated real-Time analysis of events from multiple video perspectives. However, the latency critical nature of these applications necessitates computing at the Edge of the network, close to the cameras [1]. The required Edge computing infrastructure is necessarily distributed, with cloud like capabilities such as fault tolerance, scalability, multi-Application tenancy, and security, while functioning at the unique operating environment of the Edge. Characteristics of the Edge include, highly heterogeneous hardware platforms with limited computational resources, variable latency wireless networks, and minimal physical security. To enable vision analytics at the Edge, application developers need a distributed middleware layer that provides a suitable abstraction of the Edge computing system, allowing cloud like DevOps workflow at the Edge. Middleware layers facilitate application development by providing suitable system abstractions thereby allowing the application programmers to focus on the applications needs rather than system details. In this poster, we present the requirements of such a middleware system. We propose a distributed messaging system with storage capabilities as a potential candidate for the middleware layer.