FEVA: A Federated Video Analytics Architecture for Networked Smart Cameras

Video analytics using networked smart cameras has become a core function for many applications including surveillance, object detection, AR/VR, and so on. In recent years, a number of architectures have been proposed to organize the computing and networking resources of cloud and edge cameras to collectively complete an analytics task (e.g., 3D reconstruction, multi-view re-identification). Unfortunately, in many applications, image sharing can lead to privacy con-cerns. One example is the high definition map (HD map) for autonomous driving. An HD map has a highly dynamic layer of real-time objects. Vehicles can collectively contribute videos from their onboard cameras to construct such a layer, but the video images can contain private information (e.g., the license plate numbers of front cars). In this article, we propose FEVA, a new federat-ed video analytics architecture. Intrinsically, FEVA keeps the video image data local to the edge for analytics and transmits the analytics results to the cloud for aggregation. FEVA partitions the video analytics computing tasks in a way that is priva-cy-preserving and maximizes the overall analytics accuracy under the computing and communication resource constraints of the edge devices. We show how FEVA can be used in practice by a case study using FEVA to support a video analyt-ics application on multi-view vehicles 3D reconstruction. We implement FEVA by extending the open source platform TensorFlow Federated from Google. We deploy our case in an environment with four Amazon OeepLens cameras. Our eval-uation shows that FEVA can protect privacy while effectively increasing the accuracy of the video analytics application.