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Video Activity Extraction and Reporting with Incremental Unsupervised Learning

Abstract : The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene 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 building the structure of the scene and characterize the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.
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Contributor : Jose Luis Patino Vilchis Connect in order to contact the contributor
Submitted on : Thursday, July 15, 2010 - 6:48:59 PM
Last modification on : Friday, February 4, 2022 - 3:22:53 AM
Long-term archiving on: : Friday, October 22, 2010 - 11:51:14 AM


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  • HAL Id : inria-00502836, version 1



Jose Luis Patino Vilchis, François Bremond, Murray Evans, Ali Shahrokni, James Ferryman. Video Activity Extraction and Reporting with Incremental Unsupervised Learning. 7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Aug 2010, Boston, United States. ⟨inria-00502836⟩



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