In this paper, we investigate the cultural aspects of different populations from video sequences. For that, we proposed a model that considers a series of characteristics of the pedestrians and the crowd, such as distances and speeds and performs the mapping of these characteristics in personalities, emotions, and cultural aspects. The model called Big4GD consists of four dimensions of geometric characteristics and seeks to describe the behavior of pedestrians and groups in the crowd. We performed a study of group behavior in a controlled experiment and focused on differences in two attributes that vary across cultures: (walking speed and personal distance) in three countries (India, Brazil, and Germany). We use the Fundamental Diagram theory that determines the relationship between the density and speed of individuals. We use Computer Vision methods to detect and track individuals through video sequences by generating their positions and speeds as a function of time. With these data, we analyze emergent walking speeds and densities while considering the personal distance of each individual and the neighbor in front of him/her. Our results show that human behavior is more similar in highly dense populations, i.e., individuals behave like a mass when presented with limited free personal space. The opposite result is also relevant: cultural differences can be observed at low and moderate densities, and such assumptions can be applied to computational interfaces and simulations, games, and movies. Besides, we present GeoMind, a software we developed to detect a series of characteristics from pedestrians. We also performed a practical case-study using GeoMind focusing on event detection in video sequences.

在本文中，我们从视频序列中调查了不同人群的文化方面。为此，我们提出了一个模型，该模型考虑了行人和人群的一系列特征，例如距离和速度，并在个性、情感和文化方面对这些特征进行映射。模型称为Big4GD由几何特征的四个维度组成，旨在描述人群中行人和群体的行为。我们在一项受控实验中对群体行为进行了研究，重点研究了三个国家（印度、巴西和德国）在不同文化中不同的两个属性的差异：（步行速度和个人距离）。我们使用基本图理论来确定个人的密度和速度之间的关系。我们使用计算机视觉方法通过生成作为时间函数的位置和速度来通过视频序列检测和跟踪个人。有了这些数据，我们分析了紧急步行速度和密度，同时考虑了每个人和他/她前面的邻居的个人距离。我们的结果表明，人类行为在高度密集的人群中更加相似，即当个人空间有限时，个体的行为就像一个群体。相反的结果也是相关的：可以在低密度和中等密度下观察到文化差异，并且此类假设可以应用于计算界面和模拟、游戏和电影。此外，我们提出GeoMind是我们开发的一款软件，用于检测行人的一系列特征。我们还使用GeoMind进行了实际案例研究，重点是视频序列中的事件检测。