Autor según el artículo: Sarker MMK; Rashwan HA; Akram F; Talavera E; Banu SF; Radeva P; Puig D
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: AKRAM, FARHAN / Puig Valls, Domènec Savi
Palabras clave: Visual lifelogging Self-attention model Scene classification Scene Obesity Food places recognition Egocentric photo-streams Classification Atrous convolutional networks
Resumen: © 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.
Áreas temáticas: 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
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 21693536
Direcció de correo del autor: domenec.puig@urv.cat
Identificador del autor: 0000-0002-0562-4205
Fecha de alta del registro: 2023-04-29
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://ieeexplore.ieee.org/document/8671710
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Ieee Access. 7 39069-39082
Referencia de l'ítem segons les normes APA: Sarker MMK; Rashwan HA; Akram F; Talavera E; Banu SF; Radeva P; Puig D (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 del artículo: 10.1109/ACCESS.2019.2902225
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2019
Tipo de publicación: Journal Publications
Repositori URV