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

• URL: http://arxiv.org/abs/2308.08717v1
• Date: Thu, 17 Aug 2023 00:49:44 GMT
• Title: EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
• Abstract summary: EdgeMA is a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time. We have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift.
• Score: 31.01270257565127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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