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 both in presence of complete trajectories and with tracklets provided by available detector/tracker systems.
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For more information on how to run the code, download the README or have a look at it on GitHub. If you find any bugs or need help in running the code, please contact one of the authors. Thank you!student003 or a selection of MPT-20x100.
If you use this code, please cite the following article:
Solera, F.; Calderara, S.; Cucchiara, R., "Socially Constrained Structural Learning for Groups Detection in Crowd" IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug. 2015 DOI: http://dx.doi.org/10.1109/TPAMI.2015.2470658