structured learning for social group detection in crowds

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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 both in presence of complete trajectories and with tracklets provided by available detector/tracker systems.

code and datasets

If you download this code you're half way ready to run it yourself! You'll first need to fetch some data as well.

In order to ease your first launch, we suggest you to download one of our datasets:

(detector/tracker input)

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!

demo on GVEII

For more videos, see the demo on student003 or a selection of MPT-20x100.

citation and contacts

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