基于VGGT-Count网络模型的高度聚合的游客人群对安全风险的早期警告
Jingjing Liu1, Gengan Wu1, Yao Liu2
1College of Tourism, Huaqiao University, Quanzhou, Fujian, China.
PloS one
|March 28, 2024
概括
这项研究引入了一种新的VGGT-Count模型,用于预测受欢迎目的地的游客人群密度. 该模型提高了实时监控的准确性,并有助于防止高度聚集的旅游人群 (HATC) 造成的安全风险.
科学领域:
- 计算机视觉 计算机视觉
- 旅游管理 旅游管理
- 公共安全公众安全.
背景情况:
- 大众旅游和互联网名声导致风景景区过度拥挤.
- 高度的旅游集中带来了严重的,无法控制的安全风险.
- 对于高度聚集的旅游人群 (HATC) 迫切需要有效的监测和预警系统.
研究的目的:
- 为了应对监测和预测风景区人群密度的挑战.
- 开发一个模型来预测高度聚集的旅游人群 (HATC).
- 为旅游当局提供实时监控和及时警告.
主要方法:
- 提出了一个用于预测人群密度的VGGT-Count网络模型.
- 用上海科技B和UCF-QNRF等数据集进行实验验证.
- 专注于研究高度聚集的旅游人群 (HATC) 的目标.
主要成果:
- 在基准数据集的计数准确性方面取得了实质性的改进.
- 证明了该模型对旅游景点实时监控的能力.
- 能够先进预测风景区的高游客度.
结论:
- VGGT-Count模型为管理人群密度提供了有效的解决方案.
- 及时的信息使当局能够实施预防措施,并最大限度地降低安全危险.
- 支持在旅游目的地进行主动人群控制和流量调节.
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