Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods.
Rovira i Virgili University（罗维拉-威尔吉利大学）；University of Barcelona（巴塞罗那大学）
First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is 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 phot…
Purdue University（普渡大学）；The University of Alabama（阿拉巴马大学）
Diet is an important aspect of our health. Good dietary habits can contribute to the prevention of many diseases and improve the overall quality of life. To better understand the relationship between diet and health, image-based dietary assessment systems have been developed to collect dietary information. We introduce the Automatic Ingestion Monitor (AIM), a device that can be attached to one's eye glasses. It provides an automated hands-free approach to capture eating scene images. While AIM has several advantages, images captured by the AIM are sometimes blurry. Blurry images can significan…
Imperial College London（伦敦帝国理工学院）；University of Pittsburgh（匹兹堡大学）；Baylor College Of Medicine（贝勒医学院）；University of Ghana（加纳大学）；University of Georgia（佐治亚大学）；Boston University（波士顿大学）
Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric weara…
Bernoulli Institute, University of Groningen（格罗宁根大学伯努利研究所）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
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 behavioural 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 classificat…
University of Catania（卡塔尼亚大学）；Next Vision s.r.l.（Next Vision有限公司）
We consider the problem of detecting Egocentric HumanObject Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of sta…
In object recognition research, many commonly used datasets (e.g., ImageNet and similar) contain relatively sparse distributions of object instances and views, e.g., one might see a thousand different pictures of a thousand different giraffes, mostly taken from a few conventionally photographed angles. These distributional properties constrain the types of computational experiments that are able to be conducted with such datasets, and also do not reflect naturalistic patterns of embodied visual experience. As a contribution to the small (but growing) number of multi-view object datasets that h…
Imperial College London（伦敦帝国理工学院）；Shanghai Jiao Tong University（上海交通大学）
Malnutrition is a major public health concern in low-and-middle-income countries (LMICs). Understanding food and nutrient intake across communities, households and individuals is critical to the development of health policies and interventions. To ease the procedure in conducting large-scale dietary assessments, we propose to implement an intelligent passive food intake assessment system via egocentric cameras particular for households in Ghana and Uganda. Algorithms are first designed to remove redundant images for minimising the storage memory. At run time, deep learning-based semantic segme…
We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, show…
University of Lincoln（林肯大学）；National Centre for Food Manufacturing（国家食品制造中心）；University of Aberdeen（阿伯丁大学）
Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, preparation guidelines, pack weight, storage and shelf life information (use-by / best before dates). The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks. Consequently, erroneous or illegible labeling has the potential to be highly detrimental to consumers and many other stakeholders in the supply chain. In this paper,…
Novel Multicolumn Kernel Extreme Learning Machine for Food Detection via Optimal Features from CNN
Department of Artificial Intelligence, Faculty Of Computer Science And Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia（人工智能系，计算机科学与信息技术学院，马来大学）
A rapidly growing amount of content posted online, such as food recipes, opens doors to new exciting applications at the intersection of vision and language. In this work, we aim to estimate the calorie amount of a meal directly from an image by learning from recipes people have published on the Internet, thus skipping time-consuming manual data annotation. Since there are few large-scale publicly available datasets captured in unconstrained environments, we propose the pic2kcal benchmark comprising 308,000 images from over 70,000 recipes including photographs, ingredients and instructions. To…
INTIA；UNCPBA（布宜诺斯艾利斯省中部国立大学）；CONICET（阿根廷国家科学与技术研究理事会）；University of Groningen（格罗宁根大学）；University of Barcelona（巴塞罗那大学）
The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person's patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised gre…
The University of Tokyo（东京大学）；Japan Institute of Sports Sciences（日本体育科学研究所）
We investigate image recognition of multiple food items in a single photo, focusing on a buffet restaurant application, where menu changes at every meal, and only a few images per class are available. After detecting food areas, we perform hierarchical recognition. We evaluate our results, comparing to two baseline methods.
University of Groningen（格罗宁根大学）；University of Barcelona（巴塞罗那大学）
The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person's health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of …
Wearable cameras can gather large a\-mounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a Late Fusion Ensemble strategy relying on convolutional neural …
Semantic Summarization of Egocentric Photo Stream Events
Universitat Politecnica de Catalunya（加泰罗尼亚理工大学）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Consorci Sanitari de Terrassa（特拉萨医疗联盟）
University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera 2fpm, by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label spac…
Boston University（波士顿大学）；Lori Systems LTD（洛里系统有限公司）
Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ~30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs…
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；University of Groningen（格罗宁根大学）；Imagga Technologies Ltd（Imagga技术有限公司）；Digital Spaces Living Lab（数字空间生活实验室）
EgoRelight: Egocentric Human Capture and Illumination Recovery for Relightable and Photoreal Avatar Rendering
MPI for Informatic, SIC \& VIA Research Center Saarbr \"u cken Germany；Google Mountain View USA；Google Z \"u rich Switzerland；MPI for Informatic \& VIA Research Center Saarbr \"u cken Germany；MPI for Informatic, SIC \& VIA Research Center；Google；MPI for Informatic \& VIA Research Center
Meta AI；Georgia Institute of Technology（佐治亚理工学院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Meta Reality Labs（Meta现实实验室）
Given a video captured from a first person perspective and the environment context of where the video is recorded, can we recognize what the person is doing and identify where the action occurs in the 3D space? We address this challenging problem of jointly recognizing and localizing actions of a mobile user on a known 3D map from egocentric videos. To this end, we propose a novel deep probabilistic model. Our model takes the inputs of a Hierarchical Volumetric Representation (HVR) of the 3D environment and an egocentric video, infers the 3D action location as a latent variable, and recognizes…
Facebook Reality Labs（Facebook现实实验室）；Imperial College London（伦敦帝国理工学院）；Carnegie Mellon University（卡内基梅隆大学）
Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR). The best 3D video-realistic VR avatars that minimize the uncanny effect rely on person-specific (PS) models. However, these PS models are time-consuming to build and are typically trained with limited data variability, which results in poor generalization and robustness. Major sources of variability that affects the accuracy of facial expression transfer algorithms include using different VR headsets (e.g., camera configuration…
Universitat Politecnica de Catalunya（加泰罗尼亚理工大学）；Facebook AI Research（Facebook人工智能研究院）
People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively e…
Yelp has been one of the most popular local service search engine in US since 2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant customers and business owners upload photo images to Yelp, including reviewing or advertising either food, drinks, or inside and outside decorations. It is obviously not so effective that labels for food photos rely on human editors, which is an issue should be addressed by innovative machine learning approaches. In this paper, we present a simple but effective approach which can identify up to ten kinds of food via raw photos from the ch…
IRIT, CNRS（法国国家科学研究中心图卢兹计算机科学研究所）；University of Toulouse（图卢兹大学）
Temporal segmentation of untrimmed videos and photo-streams is currently an active area of research in computer vision and image processing. This paper proposes a new approach to improve the temporal segmentation of photo-streams. The method consists in enhancing image representations by encoding long-range temporal dependencies. Our key contribution is to take advantage of the temporal stationarity assumption of photostreams for modeling each frame by its nonlocal self-similarity function. The proposed approach is put to test on the EDUB-Seg dataset, a standard benchmark for egocentric photos…
E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes
The University of Tokyo, Japan（东京大学）；National Institute of Informatics, Japan（日本信息处理学会）；The University of Osaka, Japan（大阪大学）；Advanced Telecommunications Research Institute International, Japan（国际先进电信研究机构）
