Event detection using multiple event probability sequences

Authors:
Naresh P. Cuntoor
Affiliations:
Kitware Inc., Clifton Park, NY
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
Cited 0

Recognition of Visual Activities and Interactions by Stochastic Parsing

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View-Invariant Representation and Recognition of Actions

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VERL: An Ontology Framework for Representing and Annotating Video Events

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Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models

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Activity Modeling Using Event Probability Sequences

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Abstract

We model human activities in videos using event probability sequences that detect events based on stable, state-level changes in learned hidden Markov models (HMM). The probability of an event occurring at every point along a motion trajectory is computed to form an event probability sequence. In this paper we propose extensions of the event probability sequences approach to handle multiple trajectories, in which events are associated with activities, rather than individual trajectories. Preliminary activity recognition experiments using indoor video sequences provide encouraging results.