MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams
This work addresses food intake regulation for health monitoring, but it is incremental as it applies an existing method to a new domain-specific dataset.
The paper tackled the problem of automatically recognizing food places from egocentric photo-streams to monitor a person's daily recurrences at such locations, achieving promising results on a private dataset.
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.