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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
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