Murray Evans

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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 contex-tual(More)
  • Martin J. Westgate, Ben C. Scheele, Karen Ikin, Anke Maria Hoefer, R. Matthew Beaty, Murray Evans +5 others
  • 2015
Understanding the influence of landscape change on animal populations is critical to inform biodiversity conservation efforts. A particularly important goal is to understand how urban density affects the persistence of animal populations through time, and how these impacts can be mediated by habitat provision; but data on this question are limited for some(More)
This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through(More)
In this work we present a novel approach for activity extraction and knowledge discovery from video employing fuzzy relations. Spatial 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(More)