Crowd behaviour analysis using vision has been subject to many different approaches. Multi-purpose crowd descriptors are one of the more recent approaches. These descriptors provide an opportunity to compare and categorize various types of crowds as well as classify their respective behaviours. Nevertheless, the automated calculation of descriptors which are expressed as measurements with accurate interpretation is a challenging problem. In this paper, analogies between human crowds and molecular thermodynamics systems are drawn for the measurement of crowd behaviour. Specifically, a novel descriptor is defined and measured for crowd behaviour at multiple scales. This descriptor uses the concept of Entropy for evaluating the state of crowd disorder. By results, the descriptor Entropy does indeed appear to capture the desired outcome for crowd entropy while utilizing easily detectable image features. Our new approach for machine understanding of crowd behaviour is promising, while it offers new complementary capabilities to the existing crowd descriptors, for example, as will be demonstrated, in the case of spectator crowds. The scope and performance of this descriptor are further discussed in detail in this paper.

中文翻译：

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使用视觉感应和统计力学原理的多尺度人群特征检测

使用视觉的人群行为分析已经历了许多不同的方法。多功能人群描述符是最近的方法之一。这些描述符提供了一个机会，可以对各种类型的人群进行比较和分类，以及对其各自的行为进行分类。然而，以精确的解释表示为测量值的描述符的自动计算是一个具有挑战性的问题。在本文中，人类人群和分子热力学系统之间的类比被用来测量人群的行为。具体而言，针对多个级别的人群行为定义并测量了新颖的描述符。该描述符使用熵的概念来评估人群疾病的状态。通过结果，描述符熵的确确实捕捉到了人群熵的预期结果，同时利用了易于检测的图像特征。我们的机器对人群行为的理解的新方法很有希望，同时它为现有人群描述符提供了新的补充功能，例如，如将在观众人群中展示的那样。本文将进一步详细讨论该描述符的范围和性能。