Unsupervised Activity Extraction on Long-Term Video Recordings Employing Soft Computing Relations

Abstract. In this work we present a novel approach for activity extrac-tion and knowledge discovery from video employing fuzzy relations. Spa-tial and temporal properties from detected mobile objects are modeled with fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows finding spatio-temporal patterns of activity. We present results obtained on videos correspond-ing to different sequences of apron monitoring in the Toulouse airport in France