Eating activity detection from images acquired by a wearable camera

We present an eating activity detection method via automatic detecting dining plates from images acquired chronically by a wearable camera. Convex edge segments and their combinations within each input image are modeled with respect to probabilities of belonging to candidate ellipses. Then, a dining plate is determined according to a confidence score. Finally, the presence/absence of an eating event in an image sequence is determined by analyzing successive frames. Our experimental results verified the effectiveness of this method.