Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition
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Abstract
We present data-driven techniques to augment Bag of
Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are
not known a priori. Our approach specifically addresses the
limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that
is inherent in activity streams. In addition, we also propose
the use of randomly sampled regular expressions to discover
and encode patterns in activities. We demonstrate the effectiveness of our approach in experimental evaluations where
we successfully recognize activities and detect anomalies in
four complex datasets.
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2013-06
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