Real-time video analytics on edge devices for changing scenes remains a
difficult task. As edge devices are usually resource-constrained, edge deep
neural networks (DNNs) have fewer weights and shallower architectures than
general DNNs. As a result, they only perform well in limited scenarios and are
sensitive to data drift. In this paper, we introduce EdgeMA, a practical and
efficient video analytics system designed to adapt models to shifts in
real-world video streams over time, addressing the data drift problem. EdgeMA
extracts the gray level co-occurrence matrix based statistical texture feature
and uses the Random Forest classifier to detect the domain shift. Moreover, we
have incorporated a method of model adaptation based on importance weighting,
specifically designed to update models to cope with the label distribution
shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our
results illustrate that EdgeMA significantly improves inference accuracy.

EdgeMA 是一个实际高效的视频分析系统，通过基于统计纹理特征的灰度共生矩阵提取和随机森林分类器检测领域转变，以及基于重要性加权的模型适应方法，解决了边缘设备上实时视频分析中数据漂移问题，并通过真实数据集的严格评估，结果表明 EdgeMA 显著提高了推理准确性。

EdgeMA: 为边缘设备上实时视频分析的模型适应系统

EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge  Devices

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。