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First-person (wearable) camera continually captures unscripted interactionsof the camera user with objects, people, and scenes reflecting his personal andrelational tendencies. One of the preferences of people is their interactionwith food events. The regulation of food intake and its duration has a greatimportance to protect against diseases. Consequently, this work aims to developa smart model that is able to determine the recurrences of a person on foodplaces during a day. This model is based on a deep end-to-end model forautomatic food places recognition by analyzing egocentric photo-streams. Inthis paper, we apply multi-scale Atrous convolution networks to extract the keyfeatures related to food places of the input images. The proposed model isevaluated on an in-house private dataset called "EgoFoodPlaces". Experimentalresults shows promising results of food places classification recognition inegocentric photo-streams.

MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams