Abstract
High-level (hierarchical) behaviour with long-term correlations is difficult to describe with first-order Markovian models like Hidden Markov models. We therefore discuss different approaches to synthesise generative probabilistic models for activity recognition based on different symbolic high-level description. Those descriptions of complex activities are compiled into robust generative models. The underlying assumptions for our work are (i) we need probabilistic models in robust activity recognition systems for the real world, (ii) those models should not necessarily rely on an extensive training phase and (iii) we should use available background knowledge to initialise them. We show how to construct such models based on different symbolic representations.
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Burghardt, C., Wurdel, M., Bader, S., Ruscher, G., Kirste, T. (2011). Synthesising Generative Probabilistic Models for High-Level Activity Recognition. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol 4. Atlantis Press. https://doi.org/10.2991/978-94-91216-05-3_10
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