Edge Assisted Efficient Data Annotation for Realtime Video Big Data

There is a lack of efficient video data labeling mechanism for real-time applications. Most of the labeling solutions are designed for big data offline video analytics, however emerging real-time applications with video analytics and the data-centric global trend require real-time video analytics with real-time labeling/annotation. In this paper, we present a solution that provides a per frame custom metadata that can be easily encoded and decoded and overlaid with the video frame for labeling the relevant objects/scenes. The presented solution is implemented and tested in a pilot and is leveraging edge computing capabilities to minimize the cost of using the cloud (in terms of latency and additional network resources).