移动人群估计的综合框架,用于2019年7月1日在香港举行的集会
T Edwin Chow1, Paul S F Yip2, Kwan-Po Wong3
1Department of Geography and Environmental Studies, Texas State University, San Marcos, TX 78666 USA.
概括
手动人群计数在大型活动中容易出现错误. 本研究介绍了一种集成的计算机视觉和统计方法,用于在复杂的城市环境中准确的移动人群估计.
科学领域:
- 计算机视觉 计算机视觉
- 统计建模 统计建模
- 移动人口的估计.
背景情况:
- 传统的手动人群计数是劳动密集型,容易出现错误,特别是在香港集会等大规模,长时间的活动中.
- 现有的自动化人群计数方法通常仅限于小规模的,受控的室内环境.
- 在将自动人群计数应用于大型户外城市活动方面存在重大差距.
研究的目的:
- 解决大型户外活动中手动人群估计的局限性.
- 为准确的移动人群计数提出和验证一个综合方法.
- 填补大规模移动人口估计中的经验差距.
主要方法:
- 结合捕获-重新捕获的统计方法与卷积神经网络 (CNN) 计算机视觉技术.
- 在现实世界,大型户外活动中实施了人群估计的综合方法.
- 利用复杂的城市户外景观用于人群计数应用.
主要成果:
- 在2019年香港7月1日集会上成功计入了276,970人.
- 对于人群估计,达到了95%的263,663到290,276的置信区间.
- 证明了在开放的城市路线上为数十万人进行自动人群计数的可行性.
结论:
- 综合捕获-重新捕获和CNN方法为复杂的户外环境中的移动人群估计提供了强大的解决方案.
- 这项研究为改善使用替代数据源进行人口估计提供了有价值的见解.
- 这些发现支持在社会科学和事件管理中应用先进的计算方法.
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