Security is an important topic in our contemporary world, and the ability to automate the detection of any events of interest that can take place in a crowd is of great interest to a population. We hypothesize that the detection of events in videos is correlated with significant changes in pedestrian behaviors. In this paper, we examine three different scenarios of crowd behavior, containing both the cases where an event triggers a change in the behavior of the crowd and two video sequences where the crowd and its motion remain mostly unchanged. With both the videos and the tracking of the individual pedestrians (performed in a pre-processed phase), we use Geomind, a software we developed to extract significant data about the scene, in particular, the geometrical features, personalities, and emotions of each person. We then examine the output, seeking a significant change in the way each person acts as a function of the time, that could be used as a basis to identify events or to model realistic crowd actions. When applied to the games area, our method can use the detected events to find some sort of pattern to be then used in agent simulation. Results indicate that our hypothesis seems valid in the sense that the visually observed events could be automatically detected using GeoMind.

通过分析视频中人群的几何特征、个性和情感，探讨事件检测与人群行为的相关性，并介绍了一种基于GeoMind软件的方法，可以自动检测和模拟人群中发生的事件。

通过行人几何尺寸的变化检测人群中的事件