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.
Citation:
Solera, Francesco; Calderara, Simone; Cucchiara, Rita "Socially Constrained Structural Learning for Groups Detection in Crowd" IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 38, pp. 995 -1008 , 2016 DOI: 10.1109/TPAMI.2015.2470658
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- DOI: 10.1109/TPAMI.2015.2470658