Studying the movements of crowds is important for understanding and predicting the behavior of large groups of people. When analyzing crowds, one is often interested in the long-term macro-level motions of the crowd as a whole, as opposed to the micro-level short-term movements of individuals. A high-level representation of these motions is thus desirable. In this work, we present a scalable method for detection of crowd motion patterns, i.e., spatial areas describing the dominant motions within crowds. For measuring crowd movements, we propose a fast, scalable, and low-cost method based on proximity graphs. For analyzing crowd movements, we utilize a three-stage pipeline: (1) represents the behavior of each person at each moment in time as a low-dimensional data point, (2) cluster these data points based on spatial relations, and (3) concatenate these clusters based on temporal relations. Experiments on synthetic datasets reveals our method can handle various scenarios including curved lanes and diverging flows. Evaluation on real-world datasets shows our method is able to extract useful motion patterns which could not be properly detected by existing methods. Overall, we see our work as an initial step towards rich pattern recognition.

中文翻译：

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人群运动模式的可扩展检测

研究人群的运动对于理解和预测大量人群的行为很重要。在分析人群时，人们往往对人群整体的长期宏观运动感兴趣，而不是个体的微观短期运动。因此，这些运动的高级表示是可取的。在这项工作中，我们提出了一种用于检测人群运动模式的可扩展方法，即描述人群中主要运动的空间区域。为了测量人群运动，我们提出了一种基于邻近图的快速、可扩展和低成本的方法。为了分析人群运动，我们使用了一个三阶段管道：（1）将每个人在每个时刻的行为表示为一个低维数据点，（2）基于空间关系对这些数据点进行聚类，(3) 根据时间关系连接这些集群。对合成数据集的实验表明，我们的方法可以处理各种场景，包括弯曲车道和分流。对真实世界数据集的评估表明，我们的方法能够提取现有方法无法正确检测到的有用运动模式。总的来说，我们将我们的工作视为迈向丰富模式识别的第一步。