Socially Constrained Structural Learning for Groups Detection in Crowd


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

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