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Eduardo Aguilar, Beatriz Remeseiro, Marc Bolaños, & Petia Radeva. (2018). Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. IEEE Transactions on Multimedia, 20(12), 3266–3275.
Abstract: The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.
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Roger Max Calle Quispe, Maya Aghaei Gavari, & Eduardo Aguilar Torres. (2023). Towards real-time accurate safety helmets detection through a deep learning-based method. Ingeniare. Revista chilena de ingenieria.
Abstract: Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.
These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability.
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Md.Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Estefania Talavera, Syeda Furruka Banu, Petia Radeva, et al. (2019). Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism. ACCESS - IEEE Access, 7, 39069–39082.
Abstract: 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.
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Alejandro Cartas, Petia Radeva, & Mariella Dimiccoli. (2020). Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications. ACCESS - IEEE Access, 8, 77344–77363.
Abstract: Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications.
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Alina Matei, Andreea Glavan, Petia Radeva, & Estefania Talavera. (2021). Towards Eating Habits Discovery in Egocentric Photo-Streams. ACCESS - IEEE Access, 9, 17495–17506.
Abstract: Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals.
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