深度学习在人群管理智能视频监控中的应用:系统文献综述

Andrea Camille Garcia, Jealine Eleanor E. Gorre, J. A. K. Perez, M. Samonte
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引用次数: 0

摘要

人群被定义为在同一场所聚集的人群。当人数超过正常情况时,由于大量人群可能给该地区的个人带来风险,因此过度拥挤成为安全和健康问题的一个关切。人群分析是计算机视觉领域的一个发展趋势,与人群监控相关。为了减轻与人群相关的风险,应用于监控的智能技术被用于分析人群,并监控其密度和镜头中捕捉到的人的行为。通过对过去五年中发表的与人群分析相关的各种论文的系统文献综述,介绍了过去研究人员应用的众多深度学习算法,并对其进行了评估,以提出进一步帮助人群管理的解决方案
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning in Smart Video Surveillance for Crowd Management: A Systematic Literature Review
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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