2026-07-22T18:09:52Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:12005 2024-03-12T11:56:52Z ddc:004 open_access

Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments Zheng, Qiushi Jin, Jiong Zhang, Tiehua Gao, Longxiang Xiang, Yong Fog Computing Convolution Neural Network Infrastructure-less Environment 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Qiushi Zheng and Jiong Jin and Tiehua Zhang and Longxiang Gao and Yong Xiang 2020 Is Part Of OASIcs, Volume 80, 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.Fog-IoT.2020.11 urn:nbn:de:0030-drops-120050 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Fog-IoT.2020.11 eng https://creativecommons.org/licenses/by/3.0/legalcode