Peng Liu
ABSTRACT
Deep learning with Deep Neural Networks (DNNs) can achieve much higher accuracy on many computer vision tasks than classic machine learning algorithms. Because of the high demand for both computation and storage resources, DNNs are often deployed in the cloud. Unfortunately, executing deep learning inference in the cloud, especially for real-time video analysis, often incurs high bandwidth consumption, high latency, reliability issues, and privacy concerns. Moving the DNNs close to the data source with an edge computing paradigm is a good approach to address those problems. The lack of an open source framework with a high-level API also complicates the deployment of deep learning-enabled service at the Internet edge. This paper presents EdgeEye, an edge-computing framework for real-time intelligent video analytics applications. EdgeEye provides a high-level, task-specific API for developers so that they can focus solely on application logic. EdgeEye does so by enabling developers to transform models trained with popular deep learning frameworks to deployable components with minimal effort. It leverages the optimized inference engines from industry to achieve the optimized inference performance and efficiency.
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Index Terms
- EdgeEye: An Edge Service Framework for Real-time Intelligent Video Analytics
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