Enabling Edge-Cloud Video Analytics for Robotics Applications

Abstract

Emerging deep learning-based video analytics tasks demand computation-intensive neural networks and powerful computing resources on the cloud to achieve high accuracy. Due to the latency requirement and limited network bandwidth, edge-cloud systems adaptively compress the data to strike a balance between overall analytics accuracy and bandwidth consumption. However, the degraded data leads to another issue of poor tail accuracy, which means the extremely low accuracy of a few semantic classes and video frames. Autonomous robotics applications especially value the tail accuracy performance but suffer using the prior edge-cloud systems. We present Runespoor, an edge-cloud video analytics system to manage the tail accuracy and enable emerging robotics applications. We train and deploy a super-resolution model tailored for the tail accuracy of analytics tasks on the server to significantly improves the performance on hard-to-detect classes and sophisticated frames. During online operation, we use an adaptive data rate controller to further improve the tail performance by instantly adjusting the data rate policy according to the video content. Our evaluation shows that Runespoor improves class-wise tail accuracy by up to 300%, frame-wise 90%/99% tail accuracy by up to 22%/54%, and greatly improves the overall accuracy and bandwidth trade-off.