CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale

TL;DR

CrossRoI is a system that leverages cross-camera content correlations to significantly reduce communication and computation costs in large-scale real-time video analytics, maintaining high accuracy.

Contribution

It introduces a novel two-phase approach that exploits physical correlations across cameras to optimize real-time video processing at scale.

Findings

Achieves 42%-65% reduction in network overhead

Reduces response delay by 25%-34%

Maintains over 99% query accuracy

Abstract

Video cameras are pervasively deployed in city scale for public good or community safety (i.e. traffic monitoring or suspected person tracking). However, analyzing large scale video feeds in real time is data intensive and poses severe challenges to network and computation systems today. We present CrossRoI, a resource-efficient system that enables real time video analytics at scale via harnessing the videos content associations and redundancy across a fleet of cameras. CrossRoI exploits the intrinsic physical correlations of cross-camera viewing fields to drastically reduce the communication and computation costs. CrossRoI removes the repentant appearances of same objects in multiple cameras without harming comprehensive coverage of the scene. CrossRoI operates in two phases - an offline phase to establish cross-camera correlations, and an efficient online phase for real time video inference. Experiments on real-world video feeds show that CrossRoI achieves 42% - 65% reduction for network overhead and 25% - 34% reduction for response delay in real time video analytics applications with more than 99% query accuracy, when compared to baseline methods. If integrated with SotA frame filtering systems, the performance gains of CrossRoI reach 50% - 80% (network overhead) and 33% - 61% (end-to-end delay).