We are only starting to understand how people behave when they are part of a crowd. This article presents a novel approach to the study and management of crowds. The approach comprises a device to be worn by individuals, an infrastructure to collect the information from the devices, a set of algorithms for recognizing crowd dynamics, and a set of feedback strategies to intervene in the crowd. A fundamental element of our approach is to consider crowds in terms of their texture. The crowd texture is represented through the proximity graph, a data structure that captures the spatial closeness relationship between individuals over time. We address its properties and limitations, a system architecture to measure and process it, and a few examples of insights that can be obtained from analyzing it.