A majority of the approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability
to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that
the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent
activities incorporating both input from pervasive light-weight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with
background knowledge to improve the overall recognition accuracy. To this end, we propose two Markov logic formulations for
inferring the foreground activity as well as each activities’ start and end times. We evaluate the approach on an established dataset. where it outperforms state-of-the-art algorithms for activity recognition.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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