Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence, detecting groups in crowds is becoming an important issue in modern behavior analysis. We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups, by injecting in the model socially plausible shape configurations. By taking into account social groups patterns, the system is able to outperform state of the art methods on two publicly available benchmark sets of videos. © 2013 Springer-Verlag.
Social groups detection in crowd through shape-augmented structured learning / Solera, F.; Calderara, S.. - 8156:1(2013), pp. 542-551. (Intervento presentato al convegno 17th International Conference on Image Analysis and Processing, ICIAP 2013 tenutosi a Naples, ita nel 2013) [10.1007/978-3-642-41181-6_55].
Social groups detection in crowd through shape-augmented structured learning
Solera F.;Calderara S.
2013
Abstract
Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence, detecting groups in crowds is becoming an important issue in modern behavior analysis. We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups, by injecting in the model socially plausible shape configurations. By taking into account social groups patterns, the system is able to outperform state of the art methods on two publicly available benchmark sets of videos. © 2013 Springer-Verlag.
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