Socially Constrained Structural Learning for Groups Detection in Crowd

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of
individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering
procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory,
Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal
with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on
both ground truth trajectories and tracklets previously extracted by available detector/tracker systems.