Repositori institucional URV
| URV's Author/s: | Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / AKRAM, FARHAN / Banu, Syeda Furruka / Puig Valls, Domènec Savi |
| Author, as appears in the article.: | Sarker, Md Mostafa Kamal; Rashwan, Hatem A; Akram, Farhan; Talavera, Estefania; Banu, Syeda Furruka; Radeva, Petia; Puig, Domenec |
| Author's mail: | hatem.abdellatif@urv.cat syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat |
| Author identifier: | 0000-0001-5421-1637 0000-0002-5624-1941 0000-0002-0562-4205 |
| Journal publication year: | 2019 |
| Publication Type: | Journal Publications |
| ISSN: | 21693536 |
| 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 |
| Papper original source: | Ieee Access. 7 39069-39082 |
| Abstract: | © 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. |
| Article's DOI: | 10.1109/ACCESS.2019.2902225 |
| Link to the original source: | https://ieeexplore.ieee.org/document/8671710 |
| Papper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | 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 |
| Keywords: | Visual lifelogging Self-attention model Scene classification Scene Obesity Food places recognition Egocentric photo-streams Classification Atrous convolutional networks |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-09-21 |
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