License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.Fog-IoT.2020.11
URN: urn:nbn:de:0030-drops-120050
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/12005/
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Zheng, Qiushi ; Jin, Jiong ; Zhang, Tiehua ; Gao, Longxiang ; Xiang, Yong

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

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OASIcs-Fog-IoT-2020-11.pdf (0.4 MB)

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.

BibTeX - Entry

@InProceedings{zheng_et_al:OASIcs:2020:12005,
  author =	{Qiushi Zheng and Jiong Jin and Tiehua Zhang and Longxiang Gao and Yong Xiang},
  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 =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-144-3},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{80},
  editor =	{Anton Cervin and Yang Yang},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12005},
  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}
}

Keywords: Fog Computing, Convolution Neural Network, Infrastructure-less Environment
Collection: 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)
Issue Date: 2020
Date of publication: 08.04.2020

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