Exploiting Visual Quasi-Periodicity for Automated Chewing Event Detection Using Active Appearance Models and Support Vector Mach

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 embedding of the power spectra are employed to train a Support Vector Machine (SVM) binary classifier to detect chewing events. Experimental results yielded a crossvalidated percentage agreement of 93.4%, indicating that the proposed system provides an efficient approach to automated chewing detection.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Steven Cadavid, Mohamed Abdel-Mottaleb
• Keywords: Chewing, Computer Vision, ICPR 2010, Power Spectra, Undergo Non-linear Dimensionality