计算机科学
加权
分析
边缘设备
人工智能
分类器(UML)
适应(眼睛)
概念漂移
推论
GSM演进的增强数据速率
边缘计算
深层神经网络
域适应
数据挖掘
机器学习
人工神经网络
模式识别(心理学)
实时计算
数据流挖掘
云计算
放射科
物理
光学
操作系统
医学
作者
Qianqian Wang,Nan Zhang,Xiaobo Qu,Jianzong Wang,Jiguang Wan,Guokuan Li,Kaiyu Hu,Guilin Jiang,Jing Xiao
标识
DOI:10.1007/978-981-99-8079-6_23
摘要
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.
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