Crowd space: a predictive crowd analysis technique
Crowd space: a predictive crowd analysis technique
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人群空间：预测人群分析技术
DOI：
10.1145/3272127.3275079
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发表时间：
2019
影响因子：
6.2
通讯作者：
Guy, Stephen J.
中科院分区：
文献类型：
--
作者：
Karamouzas, Ioannis;Sohre, Nick;Hu, Ran;Guy, Stephen J.
在过去的二十年里，模拟人群的方法不断涌现。随着不同方法的数量及其复杂性的增加，期望研究人员和用户跟上所有可能的选择和权衡变得越来越不现实。因此，我们认为需要能够帮助人群模拟的领域专家和非专家用户就不同场景中使用的最佳模拟方法做出高层决策的工具。在本文中，我们利用来自人群的轨迹数据和机器学习技术来学习流形，该流形捕获人类在现实生活中遇到的代表性局部导航场景。我们展示了这种流形在人群研究中的适用性，包括分析模拟精度的趋势，以及创建自动化系统以帮助为给定场景选择合适的模拟方法。
Over the last two decades there has been a proliferation of methods for simulating crowds of humans. As the number of different methods and their complexity increases, it becomes increasingly unrealistic to expect researchers and users to keep up with all the possible options and trade-offs. We therefore see the need for tools that can facilitate both domain experts and non-expert users of crowd simulation in making high-level decisions about the best simulation methods to use in different scenarios. In this paper, we leverage trajectory data from human crowds and machine learning techniques to learn a manifold which captures representative local navigation scenarios that humans encounter in real life. We show the applicability of this manifold in crowd research, including analyzing trends in simulation accuracy, and creating automated systems to assist in choosing an appropriate simulation method for a given scenario.