Computer Vision Laboratory, GE Global Research, Niskayuna, NY 12309, USA.
IEEE Trans Pattern Anal Mach Intell. 2012 May;34(5):1003-16. doi: 10.1109/TPAMI.2011.176.
Building upon state-of-the-art algorithms for pedestrian detection and multi-object tracking, and inspired by sociological models of human collective behavior, we automatically detect small groups of individuals who are traveling together. These groups are discovered by bottom-up hierarchical clustering using a generalized, symmetric Hausdorff distance defined with respect to pairwise proximity and velocity. We validate our results quantitatively and qualitatively on videos of real-world pedestrian scenes. Where human-coded ground truth is available, we find substantial statistical agreement between our results and the human-perceived small group structure of the crowd. Results from our automated crowd analysis also reveal interesting patterns governing the shape of pedestrian groups. These discoveries complement current research in crowd dynamics, and may provide insights to improve evacuation planning and real-time situation awareness during public disturbances.
基于行人检测和多目标跟踪的最新算法，并受到人类集体行为社会学模型的启发，我们自动检测一起出行的小群体。这些群组是通过使用基于成对接近度和速度定义的广义对称 Hausdorff 距离的自下而上分层聚类发现的。我们在真实世界行人场景的视频上对结果进行了定量和定性验证。在有人工编码的地面实况的情况下，我们发现我们的结果与人群中人类感知的小群体结构之间存在显著的统计学一致性。我们的自动人群分析结果还揭示了一些有趣的模式，这些模式支配着行人群体的形状。这些发现补充了当前的人群动力学研究，并可能为改善疏散规划和公共干扰期间的实时态势感知提供见解。
