Exploiting visual quasi-periodicity for automated chewing event detection using active appearance models and support vector machines

Steven Cadavid, Mohamed Abdel-Mottaleb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We present a method that automatically detects chewing events in surveillance video of a subject. Firstly, an Active Appearance Model (AAM) is used to track a subject's face across the video sequence. It is observed that the variations in the AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between chewing and non-chewing facial actions such as talking. A feature representation is constructed by applying spectral analysis to a temporal window of model parameter values. The estimated power spectra subsequently undergo non-linear dimensionality reduction via spectral regression. The low-dimensional representations of the power spectra are employed to train a Support Vector Machine (SVM) binary classifier to detect chewing events. Experimental results yielded a cross-validated percentage agreement of 93.4%, indicating that the proposed system provides an efficient approach to automated chewing detection.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages1714-1717
Number of pages4
DOIs
StatePublished - Nov 18 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Cadavid, S., & Abdel-Mottaleb, M. (2010). Exploiting visual quasi-periodicity for automated chewing event detection using active appearance models and support vector machines. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 1714-1717). [5597478] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.424