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.

Socially Constrained Structural Learning for Groups Detection in Crowd / Solera, Francesco; Calderara, Simone; Cucchiara, Rita. - In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. - ISSN 0162-8828. - STAMPA. - 38 (5):(2016), pp. 995-1008. [10.1109/TPAMI.2015.2470658]

Socially Constrained Structural Learning for Groups Detection in Crowd

SOLERA, FRANCESCO;CALDERARA, Simone;CUCCHIARA, Rita
2016

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.
2016
38 (5)
995
1008
Socially Constrained Structural Learning for Groups Detection in Crowd / Solera, Francesco; Calderara, Simone; Cucchiara, Rita. - In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. - ISSN 0162-8828. - STAMPA. - 38 (5):(2016), pp. 995-1008. [10.1109/TPAMI.2015.2470658]
Solera, Francesco; Calderara, Simone; Cucchiara, Rita
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1074269
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