Unsupervised activity discovery and characterization from event-streams
Published 26 July 2005
Roszilah Hamid, Siddhartha Maddi, Amos Johnson, Aaron Bobick, Irfan Essa, Charles L. Isbell
Citations24
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TL;DR
It is shown how modeling an activity as a variable length Markov process, can be used to discover recurrent event-motifs to characterize the discovered activity-classes and the competence and generalizability of this proposed framework is shown.
Abstract
We present a framework to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, using their local event statistics.
Keywords
Computer Science
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