title: Socially Constrained Structural Learning for Groups Detection in Crowd.
abstract: 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.
authors.forename: Francesco
authors.initials: F
authors.lastname: Solera
authors.forename: Simone
authors.initials: S
authors.lastname: Calderara
authors.forename: Rita
authors.initials: R
authors.lastname: Cucchiara