Real Time Detection of Social Interactions in Surveillance Video

In this paper we present a novel method to detect the presence of social interactions occurring in a surveillance scenario. The algorithm we propose complements motion features with proxemics cues, so as to link the human motion with the contextual and environmental information. The extracted features are analyzed through a multi-class SVM. Testing has been carried out distinguishing between casual and intentional interactions, where intentional events are further subdivided into normal and abnormal behaviors. The algorithm is validated on benchmark datasets, as well as on a new dataset specifically designed for interactions analysis.

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