http://10.10.120.238:8080/xmlui/handle/123456789/860
Title: | Big data analytics for video surveillance |
Authors: | Subudhi B.N. Rout D.K. Ghosh A. |
Keywords: | Big Data Big Data Analytics for video Data Science Video surveillance |
Issue Date: | 2019 |
Publisher: | Springer New York LLC |
Abstract: | This article addresses the usage and scope of Big Data Analytics in video surveillance and its potential application areas. The current age of technology provides the users, ample opportunity to generate data at every instant of time. Thus in general, a tremendous amount of data is generated every instant throughout the world. Among them, amount of video data generated is having a major share. Education, healthcare, tours and travels, food and culture, geographical exploration, agriculture, safety and security, entertainment etc., are the key areas where a tremendous amount of video data is generated every day. A major share among it are taken by the daily used surveillance data captured from the security purpose camera and are recorded everyday. Storage, retrieval, processing, and analysis of such gigantic data require some specific platform. Big Data Analytics is such a platform, which eases this analysis task. The aim of this article is to investigate the current trends in video surveillance and its applications using Big Data Analytics. It also aims to focus on the research opportunities for visual surveillance in Big Data frameworks. We have reported here the state-of-the-art surveillance schemes for four different imaging modalities: conventional video scene, remotely sensed video, medical diagnostics, and underwater surveillance. Several works were reported in this research field over recent years and are categorized based on the challenges solved by the researchers. A list of tools used for video surveillance using Big Data framework is presented. Finally, research gaps in this domain are discussed. © 2019, Springer Science+Business Media, LLC, part of Springer Nature. |
URI: | https://dx.doi.org/10.1007/s11042-019-07793-w http://localhost:8080/xmlui/handle/123456789/860 |
ISSN: | 1380-7501 |
Appears in Collections: | Journal Article |
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