Cost effective processing of detection-driven video analytics at the edge

Abstract

We demonstrate a real-time video analytics system for applications that use objection detection models on incoming frames as part of their computation pipeline. Through edge-cloud collaboration, we show how a reinforcement learning based agent can skip successive video frames while keeping the object detection results almost intact for end applications.