Deep Learning in Smart Video Surveillance for Crowd Management: A Systematic Literature Review
Authors: 
Andrea Camille Garcia, Jealine Eleanor Gorre, Joshua Angelo Karl Perez, Mary Jane SamonteAuthors Info & Claims
Andrea Camille Garcia, Jealine Eleanor Gorre, Joshua Angelo Karl Perez, Mary Jane SamonteAuthors Info & ClaimsPages 144 - 150
Abstract
A crowd is defined as a gathering of people in the same premises. When the number of people exceeds normal conditions, overcrowding becomes a concern in safety and health-related matters due to the risks that a large crowd can impose on the individuals present in the area. Crowd analysis is a growing trend in computer vision related to the concerns in crowd monitoring. To alleviate risks related to crowds, intelligent techniques applied to surveillance are used to analyze a crowd and to monitor its density and the behavior of people captured in footage. Through a systematic literature review of various papers published in the last five years related to crowd analysis, the numerous deep learning algorithms applied in past researchers are presented and are assessed to come up with a solution that will further aid in crowd management
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Published In
June 2021
241 pages
ISBN:9781450389723
DOI:10.1145/3473141
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Published: 05 October 2021
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ICFET 2021
ICFET 2021: 2021 The 7th International Conference on Frontiers of Educational Technologies
June 4 - 7, 2021
Bangkok, Thailand
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