  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.
