Activity discovery from video employing soft computing relations

The present work presents a novel approach for activity extraction and knowledge discovery from video. Spatial and temporal properties from detected mobile objects are modeled employing 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 employ trajectory-based analysis of mobiles in the video to discover the points of entry and exit of mobiles appearing in the scene and ultimately deduce the different areas of activity in the scene. These areas can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix of the mobile fuzzy spatial relations. Discovered activity zones and spatio-temporal patterns of activity can be labeled in a human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.