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Realizing video analytic service in the fog-based infrastructure-less environments

Version 2 2024-06-04, 04:00
Version 1 2020-05-05, 10:15
conference contribution
posted on 2024-06-04, 04:00 authored by Q Zheng, J Jin, T Zhang, Longxiang GaoLongxiang Gao, Yong XiangYong Xiang
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.

History

Volume

80

Pagination

1-9

Location

Sydney, N.S.W.

Open access

  • Yes

Start date

2020-04-21

End date

2020-04-21

ISSN

2190-6807

ISBN-13

9783959771443

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Cervin A, Yang Y

Title of proceedings

Fog-IOT 2020 : Proceedings of the 2nd Workshop on Fog Computing and the IoT

Event

Fog Computing and the IoT. Workshop (2nd : 2020 : Sydney, N.S.W.)

Publisher

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing

Place of publication

Saarbrücken/Wadern,Germany

Series

Fog Computing and the IoT Workshop

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