Autor segons l'article: Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Talavera, Estefania; Banu, Syeda Furruka; Radeva, Petia; Puig, Domenec
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / AKRAM, FARHAN / Banu, Syeda Furruka / Puig Valls, Domènec Savi
Paraules clau: Visual lifelogging Self-attention model Scene classification Scene Obesity Food places recognition Egocentric photo-streams Classification Atrous convolutional networks
Resum: © 2013 IEEE. Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called 'EgoFoodPlaces' that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the 'EgoFoodPlaces' dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3.
Àrees temàtiques: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 21693536
Adreça de correu electrònic de l'autor: hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
Identificador de l'autor: 0000-0001-5421-1637 0000-0002-5624-1941 0000-0002-0562-4205
Data d'alta del registre: 2024-09-21
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://ieeexplore.ieee.org/document/8671710
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Ieee Access. 7 39069-39082
Referència de l'ítem segons les normes APA: Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Talavera, Estefania; Banu, Syeda Furruka; Radeva, Petia; Puig, Domenec (2019). Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism. Ieee Access, 7(), 39069-39082. DOI: 10.1109/ACCESS.2019.2902225
DOI de l'article: 10.1109/ACCESS.2019.2902225
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2019
Tipus de publicació: Journal Publications
Repositori URV