The large scale use of real-time computer vision for IoT applications faces challenges of big data streams, complex processing, low latency requirements, and data privacy concerns. Edge computing allows data to be processed close to the source, vastly reducing the data that needs to be sent to the cloud, thus reducing network bandwidth requirements, and lowering application latency. Additionally, sensitive video streams can be confined to the privacy perimeter of the end-user. However, current IoT edge middleware are designed for low data rate sensor applications, and do not satisfy the demanding needs of computer vision-based IoT.

In this paper, we present the design and implementation of a novel edge gateway targeted specifically at emerging IoT computer vision applications. The proposed edge gateway enables realization of multiple vision algorithms at the edge from a single camera stream. Furthermore, unlike existing edge gateways available from public cloud service providers, the proposed gateway is vendor-neutral, and capable of connecting to multiple cloud providers. This allows for increased application resilience, lower costs, and avoids cloud vendor lock-in. We experimentally evaluate the performance of the proposed edge gateway for multiple computer vision applications, and multiple public clouds.