Recognizing human activities using a layered markov architecture

Authors:
Michael Glodek;Georg Layher;Friedhelm Schwenker;Günther Palm
Affiliations:
Institute of Neural Information Processing, University of Ulm, Ulm, Germany;Institute of Neural Information Processing, University of Ulm, Ulm, Germany;Institute of Neural Information Processing, University of Ulm, Ulm, Germany;Institute of Neural Information Processing, University of Ulm, Ulm, Germany
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

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Abstract

In the field of human computer interaction (HCI) the detection and classification of human activity patterns has become an important challenge. The problem can be understood as a specific problem of pattern recognition which addresses three topics, namely fusion of multiple modalities, spatio-temporal structures and a vast variety of pattern appearances the more abstract a pattern gets. In order to approach the problem, we propose a layered architecture which decomposes temporal patterns into elementary sub-patterns. Within each layer the patterns are detected using Markov models. The results of a layer are passed to the next successive layer such that on each layer the temporal granularity and the complexity of patterns increases. A dataset containing activities in an office scenario was recorded. The activities are decomposed to basic actions which are detected on the first layer. We evaluated a two-layered architecture using the dataset showing the feasibility of the approach.