监控视频密集人群的人数统计系统设计
The Design of the People Counting System for Monitoring Video-Intensive Crowds
DOI: 10.12677/JISP.2020.94024, PDF,  被引量    科研立项经费支持
作者: 沈礼文, 邱 钊*, 李 超, 蔡金晔：海南大学计算机与网络空间安全学院，海南 海口；彭贵超：国家计算机网络应急技术处理协调中心海南分中心，海南 海口；黄 萍：海南大学理学院，海南 海口
关键词: 密集人群；视频监控；人群计数；YOLO V4；Dense Crowd； Video Surveillance； Crowd Count； YOLO V4
摘要: 随着社会的不断发展，人群密集的场所随处可见。对监控视频下的人员进行统计分析，实现人数统计算法，可以为城市公共资源优化配置、安保人员调度、安全管理等提供有效的技术手段。本文基于YOLO V4平台，采用深度学习算法来识别监控视频的人，并加入统计算法对识别出的人进行统计计算，实现视频监控下的人数统计。
Abstract: With the continuous development of society, crowded places can be seen everywhere. Performing statistical analysis on the personnel under surveillance video and realizing the number counting algorithm can provide effective technical means for the optimal allocation of urban public resources, security personnel scheduling, and safety management. Based on the YOLO V4 platform, this article uses a deep learning algorithm to identify people monitoring video, and adds a statistical algorithm to perform statistical calculations on the identified people to realize the number of people under video surveillance.
文章引用：沈礼文, 邱钊, 彭贵超, 黄萍, 李超, 蔡金晔. 监控视频密集人群的人数统计系统设计[J]. 图像与信号处理, 2020, 9(4): 202-210. https://doi.org/10.12677/JISP.2020.94024

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