Region-based Content Enhancement for Efficient Video Analytics at the Edge

TL;DR

RegenHance is a system that selectively enhances important video regions to improve accuracy and throughput in edge analytics, reducing computational costs and increasing efficiency.

Contribution

It introduces a novel region-based content enhancement approach with a macroblock predictor, region-aware enhancer, and resource-aware execution planner for edge video analytics.

Findings

Improves analytical accuracy by 10-19%.

Achieves 2-3x throughput over frame-based methods.

Prototyped on five heterogeneous edge devices.

Abstract

Video analytics is widespread in various applications serving our society. Recent advances of content enhancement in video analytics offer significant benefits for the bandwidth saving and accuracy improvement. However, existing content-enhanced video analytics systems are excessively computationally expensive and provide extremely low throughput. In this paper, we present region-based content enhancement, that enhances only the important regions in videos, to improve analytical accuracy. Our system, RegenHance, enables high-accuracy and high-throughput video analytics at the edge by 1) a macroblock-based region importance predictor that identifies the important regions fast and precisely, 2) a region-aware enhancer that stitches sparsely distributed regions into dense tensors and enhances them efficiently, and 3) a profile-based execution planer that allocates appropriate resources for enhancement and analytics components. We prototype RegenHance on five heterogeneous edge devices. Experiments on two analytical tasks reveal that region-based enhancement improves the overall accuracy of 10-19% and achieves 2-3x throughput compared to the state-of-the-art frame-based enhancement methods.