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• W2067202211 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." @default.
• W2067202211 created "2016-06-24" @default.
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• W2067202211 date "2013-06-01" @default.
• W2067202211 modified "2023-09-25" @default.
• W2067202211 title "Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition" @default.
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• W2067202211 doi "https://doi.org/10.1109/cvpr.2013.338" @default.
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