Real-time intelligent video processing on embedded devices with low power consumption can be useful for applications like drone surveillance, smart cars, and more. However, the limited resources of embedded devices is a challenging issue for effective embedded computing. Most of the existing work on this topic focuses on single device based solutions, without the use of cloud computing mechanisms for parallel processing to boost performance. In this paper, we propose a cloud platform for real-time video processing based on embedded devices. Eight NVIDIA Jetson TX1 and three Jetson TX2 GPUs are used to construct a streaming embedded cloud platform (SECP), on which Apache Storm is deployed as the cloud computing environment for deep learning algorithms (Convolutional Neural Networks — CNNs) to process video streams. Additionally, self-managing services are designed to ensure that this platform can run smoothly and stably, in the form of a metric sensor, a bottleneck detector and a scheduler. This platform is evaluated in terms of processing speed, power consumption, and network throughput by running various deep learning algorithms for object detection. The results show the proposed platform can run deep learning algorithms on embedded devices while meeting the high scalability and fault tolerance required for real-time video processing.
Zhang Weishan, Sun Haoyun, Zhao Dehai, Xu Liang, Liu Xin, Ning Huansheng, Zhou Jiehan, Guo Yi, Yang Su
A1 Journal article – refereed
Place of publication:
W. Zhang et al., “A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices,” in IEEE Transactions on Cloud Computing. doi: 10.1109/TCC.2019.2894621
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