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Network virtualization for real-time processing of object detection using deep learning
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DOI：10.1007/s11042-020-09603-0.png)
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Abstract
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These days, networked cameras are used in various applications using deep learning. In particular, as the deep learning technology for image processing develops, image-based application services using networked camera are expanding. Object detections are the representative application in the image-based applications. Images from the networked camera are transmitted to a deep learning machine, which performs object detection using a deep neural network (DNN) algorithm. For real-time processing of the object detection, lightweight of the image learning steps is needed. Both preprocessing of training sets and lightweight learning models can reduce computing loads for image learning. However, it is most important to receive video frames from the network camera without delay. In this paper, we provide a way for the learning machine to receive video frames with minimal delay. The proposed method is a kind of network virtualization for image-based object detection. It monitors network the status of available network interfaces in networked cameras. When a camera transmit video frames, the virtualized module determines the appropriate network interface to reduce delay. The performance of the proposed method is evaluated in the image-based object detection system using deep learning.
Keywords:
Network virtualization
Real-time processing
Object detection
Deep learning
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