Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/14816
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNatesha B.V.
dc.contributor.authorGuddeti R.M.R.
dc.date.accessioned2021-05-05T10:15:49Z-
dc.date.available2021-05-05T10:15:49Z-
dc.date.issued2021
dc.identifier.citationAdvances in Intelligent Systems and Computing , Vol. 1176 , , p. 747 - 754en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-15-5788-0_70
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14816-
dc.description.abstractWith the rapid growth in the use of IoT devices in monitoring and surveillance environment, the amount of data generated by these devices is increased exponentially. There is a need for efficient computing architecture to push the intelligence and data processing close to the data source nodes. Fog computing will help us to process and analyze the video at the edge of the network and thus reduces the service latency and network congestion. In this paper, we develop fog computing infrastructure which uses the deep learning models to process the video feed generated by the surveillance cameras. The preliminary experimental results show that using different deep learning models (DNN and SNN) at the different levels of fog infrastructure helps to process the video and classify the vehicle in real time and thus service the delay-sensitive applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.titleFog-Based Video Surveillance System for Smart City Applicationsen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.