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Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition

Lookup NU author(s): Dr Thomas Ploetz

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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 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.

Publication metadata

Author(s): Bettadapura V, Schindler G, Ploetz T, Essa I

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013)

Year of Conference: 2013

Pages: 2619-2626

ISSN: 1063-6919

Publisher: IEEE

URL: http://dx.doi.org/10.1109/CVPR.2013.338

DOI: 10.1109/CVPR.2013.338

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