Mid-level Features for Categorization of Social Interactions in Public Spaces

The paper proposes mid-level features for socio-cognitive classification of crowd behavior in public spaces, particularly in the context of monitoring social interactions during, e.g., pandemic restrictions. The classification method follows a recently proposed categorization [37]. The features are built using statistics obtained from detection and tracking results forc crowd components, i.e. individuals and their groups (any typical detectors and trackers can be used). The features are defined by static (if obtained from the current frame) or dynamic (if derived from consecutive frames) parameters characterizing the crowd. Subsequently, the features extracted from a number of most recent frames are fed into a fully-connected shallow neural network to identify the type of social interactions in the monitored space. The experimental feasibility study shows encouraging performances of the approach. In particular, the results are far more discriminative than in the other solution (which, at the moment, is the only publicly known benchmark).