Video Activity Extraction and Reporting with Incremental Unsupervised Learning

The present work presents a new method for activity extractionand reporting from video based on the aggregationof fuzzy relations. Trajectory clustering is first employedmainly to discover the points of entry and exit of mobiles appearingin the scene. In a second step, proximity relationsbetween resulting clusters of detected mobiles and contextualelements from the scene are modeled employing fuzzyrelations. These can then be aggregated employing typicalsoft-computing algebra. A clustering algorithm based onthe transitive closure calculation of the fuzzy relations allowsbuilding the structure of the scene and characterisesthe ongoing different activities of the scene. Discovered activityzones can be reported as activity maps with differentgranularities thanks to the analysis of the transitive closurematrix. Taking advantage of the soft relation properties, activityzones and related activities can be labeled in a morehuman-like language. We present results obtained on realvideos corresponding to apron monitoring in the Toulouseairport in France.

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