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.
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Glodek, M., Layher, G., Schwenker, F., Palm, G. (2012). Recognizing Human Activities Using a Layered Markov Architecture. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_85
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DOI: https://doi.org/10.1007/978-3-642-33269-2_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
Online ISBN: 978-3-642-33269-2
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