Understanding dyadic interactions applying proxemic theory on videosurveillance trajectories
Understanding social and collective people behaviour in open spaces is one of the frontier of modern video surveillance. Many sociological theories, and proxemics in particular, have been proved their validity as a support for classifying and interpreting human behaviour. Proxemics suggest some simple but effective behavioural rules, useful to understand what people are doing and their social involvement with other individuals. In this paper we propose to extend the proxemics analysis along the time and provide a solution for analysing sequences of proxemic states computed between trajectories of people pairs (dyads). Trajectories, computed from videosurveillance videos, are first analysed and converted to a sequence of symbols according to proxemic theory. Then an elastic measure for comparing those sequences is introduced. Finally, interactions are classified both in an off-line unsupervised way and in an on-line fashion. Results on videosurveillance data, demonstrate that sequences of proxemic states can be effective in characterizing mutual interactions and experiments in capturing the most frequent dyads interactions and on-line classifying them when a labelled training set is available are proposed.