  First-person (wearable) camera continually captures unscripted interactions
of the camera user with objects, people, and scenes reflecting his personal and
relational tendencies. One of the preferences of people is their interaction
with food events. The regulation of food intake and its duration has a great
importance to protect against diseases. Consequently, this work aims to develop
a smart model that is able to determine the recurrences of a person on food
places during a day. This model is based on a deep end-to-end model for
automatic food places recognition by analyzing egocentric photo-streams. In
this paper, we apply multi-scale Atrous convolution networks to extract the key
features related to food places of the input images. The proposed model is
evaluated on an in-house private dataset called "EgoFoodPlaces". Experimental
results shows promising results of food places classification recognition in
egocentric photo-streams.
