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).

CrossRoI 是一种资源有效的系统，通过利用视频内容的关联性和冗余性在规模上实现实时视频分析，其中包括离线相位建立跨相机关系的决策，以及高效在线相位的实时视频推理，同时比基线方法具有更高的查询准确性和较高的性能收益。