Group analytics and insights for public spaces

Abstract

Detecting the group context of an individual (i.e., whether an individual is alone or part of a group) in crowded public spaces, such as shopping malls, is an important goal with many practical applications. However, in crowded indoor spaces, understanding the group-dependent movement behavior is a non-trivial problem as: (1) detecting groups is hard as the density ensures that at any location, a large number of people are moving together, (2) location tracking in many real-world venues is either absent or not very accurate, and (3) indoor mobility models that take into account group attributes (such as group size) are rare. In this paper, we first introduce GruMon, a platform for near real-time group monitoring in dense, public spaces, and then demonstrate how the movement & residency properties of individuals are significantly affected when they are in groups.