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

Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Estefania Talavera, Syeda Furruka Banu, Petia Radeva, Domenec Puig; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Abstract

First-person(wearable)cameracontinuallycapturesunscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of peopleis 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 in egocentric photo-streams.

Related Material

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[bibtex]
@InProceedings{Sarker_2018_ECCV_Workshops,
author = {Mostafa Kamal Sarker, Md. and Rashwan, Hatem A. and Talavera, Estefania and Furruka Banu, Syeda and Radeva, Petia and Puig, Domenec},
title = {MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}