In this chapter we address the problem of partitioning social gatherings into interacting groups in egocentric scenarios. People in the scene are tracked, and their head pose and 3D location are estimated. Following the formalism of the f-formation, we define as regards the orientation and distance inherently social pairwise features capable of describing how two people stand in relation to one another. We present a structural SVM-based approach to learn how to weight each component of the feature vector depending on the social situation being applied to. To better understand the social dynamics, we also estimate what we call the social relevance of each subject in a group using a saliency attentive model. Extensive tests on two publicly available datasets show that our solution achieves encouraging results when detecting social groups and their relevant subjects in challenging egocentric scenarios.

Recognizing social relationships from an egocentric vision perspective

Serra G.;
2018-01-01

Abstract

In this chapter we address the problem of partitioning social gatherings into interacting groups in egocentric scenarios. People in the scene are tracked, and their head pose and 3D location are estimated. Following the formalism of the f-formation, we define as regards the orientation and distance inherently social pairwise features capable of describing how two people stand in relation to one another. We present a structural SVM-based approach to learn how to weight each component of the feature vector depending on the social situation being applied to. To better understand the social dynamics, we also estimate what we call the social relevance of each subject in a group using a saliency attentive model. Extensive tests on two publicly available datasets show that our solution achieves encouraging results when detecting social groups and their relevant subjects in challenging egocentric scenarios.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1177075
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