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DOI: 10.4230/OASIcs.Fog-IoT.2020.11

Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

Authors: Qiushi Zheng, Jiong Jin, Tiehua Zhang, Longxiang Gao, and Yong Xiang

Published in: OASIcs, Volume 80, 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)

Abstract

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process.

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Qiushi Zheng, Jiong Jin, Tiehua Zhang, Longxiang Gao, and Yong Xiang. Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments. In 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020). Open Access Series in Informatics (OASIcs), Volume 80, pp. 11:1-11:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{zheng_et_al:OASIcs.Fog-IoT.2020.11,
  author =	{Zheng, Qiushi and Jin, Jiong and Zhang, Tiehua and Gao, Longxiang and Xiang, Yong},
  title =	{{Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments}},
  booktitle =	{2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)},
  pages =	{11:1--11:9},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-144-3},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{80},
  editor =	{Cervin, Anton and Yang, Yang},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Fog-IoT.2020.11},
  URN =		{urn:nbn:de:0030-drops-120050},
  doi =		{10.4230/OASIcs.Fog-IoT.2020.11},
  annote =	{Keywords: Fog Computing, Convolution Neural Network, Infrastructure-less Environment}
}

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Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

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