In a wearable camera video, we see what the camera wearer sees. While this makes it easy to know roughly what he chose to look at, it does not immediately reveal when he was engaged with the environment. Specifically, at what moments did his focus linger, as he paused to gather more information about something he saw? Knowing this answer would benefit various applications in video summarization and augmented reality, yet prior work focuses solely on the "what" question (estimating saliency, gaze) without considering the "when" (engagement). We propose a learning-based approach that uses long-term egomotion cues to detect engagement, specifically in browsing scenarios where one frequently takes in new visual information (e.g., shopping, touring). We introduce a large, richly annotated dataset for ego-engagement that is the first of its kind. Our approach outperforms a wide array of existing methods. We show engagement can be detected well independent of both scene appearance and the camera wearer's identity.
University of the Witwatersrand（威特沃特斯兰德大学）；Southwest Jiaotong University（西南交通大学）
To perform contingent teaching and be responsive to students' needs during class, lecturers must be able to quickly assess the state of their audience. While effective teachers are able to gauge easily the affective state of the students, as class sizes grow this becomes increasingly difficult and less precise. The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect. The focus is primarily on recognising engagement or lack thereof. Student engagement is labelled based on behaviour and postures that are common to classroom settings. …
Student engagement is an important factor in meeting the goals of virtual learning programs. Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Many existing approaches solve video-based engagement measurement using the traditional frameworks of binary classification (classifying video snippets into engaged or disengaged classes), multi-class classification (classifying video snippets into multiple classes corresponding to different levels of engagement), or regression (estimating a co…
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…
University of Barcelona（巴塞罗那大学）；Computer Vision Centre（计算机视觉中心）
Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand re…
Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
Furhat Robotics；University of Naples Federico II（那不勒斯费德里科二世大学）；Division of Speech, Music and Hearing, KTH Royal Institute of Technology（语音、音乐和听觉研究所，皇家理工学院）
University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolutio…
Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating
An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications
KITE – Toronto Rehab – University Health Network（KITE – 多伦多康复研究所 – 大学健康网络）；Rehabilitation Sciences Institute, University of Toronto（多伦多大学康复科学研究所）；Harvard Medical School, Harvard University（哈佛大学医学院）
Meta Reality Labs Research（Meta现实实验室）；National University of Singapore（新加坡国立大学）
Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and 1M fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal …
Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）
Automatic detection of students' engagement in online learning settings is a key element to improve the quality of learning and to deliver personalized learning materials to them. Varying levels of engagement exhibited by students in an online classroom is an affective behavior that takes place over space and time. Therefore, we formulate detecting levels of students' engagement from videos as a spatio-temporal classification problem. In this paper, we present a novel end-to-end Residual Network (ResNet) and Temporal Convolutional Network (TCN) hybrid neural network architecture for students' …
Detecting Activities of Daily Living in Egocentric Video to Contextualize Hand Use at Home in Outpatient Neurorehabilitation Settings
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；KITE - Toronto Rehabilitation Institute, University Health Network（大学健康网络KITE-多伦多康复研究所）；Rehabilitation Sciences Institute, University of Toronto（多伦多大学康复科学研究所）；Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto（多伦多大学爱德华·S·罗杰斯 Sr. 电气与计算机工程系）
ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（上海交通大学图像通信与网络工程研究所）；Institut Universitaire de France (IUF), University of Nantes（法国南特大学机构大学（IUF））
Samsung AI Center Cambridge（三星剑桥人工智能中心）；Adobe Research（奥多比研究院）
Egocentric videos capture sequences of human activities from a first-person perspective and can provide rich multimodal signals. However, most current localization methods use third-person videos and only incorporate visual information. In this work, we take a deep look into the effectiveness of audiovisual context in detecting actions in egocentric videos and introduce a simple-yet-effective approach via Observing, Watching, and Listening (OWL). OWL leverages audiovisual information and context for egocentric temporal action localization (TAL). We validate our approach in two large-scale data…
Augmented reality devices have the potential to enhance human perception and enable other assistive functionalities in complex conversational environments. Effectively capturing the audio-visual context necessary for understanding these social interactions first requires detecting and localizing the voice activities of the device wearer and the surrounding people. These tasks are challenging due to their egocentric nature: the wearer's head motion may cause motion blur, surrounding people may appear in difficult viewing angles, and there may be occlusions, visual clutter, audio noise, and bad …
SmartFactory-KL（凯泽斯劳滕智能工厂）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Detecting small objects in video streams of head-worn augmented reality devices in near real-time is a huge challenge: training data is typically scarce, the input video stream can be of limited quality, and small objects are notoriously hard to detect. In industrial scenarios, however, it is often possible to leverage contextual knowledge for the detection of small objects. Furthermore, CAD data of objects are typically available and can be used to generate synthetic training data. We describe a near real-time small object detection pipeline for egocentric perception in a manual assembly scen…
Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；RIKEN Center for Advanced Intelligence Project（日本理化学研究所先进智能项目中心）；RIKEN（日本理化学研究所）；Systems Research Institute（系统研究所）；Polish Academy of Sciences（波兰科学院）；Nicolaus Copernicus University（尼古拉哥尔斯基大学）；University of Rome Sapienza（罗马萨皮恩扎大学）
Measuring hand use in the home after cervical spinal cord injury using egocentric video
KITE Research Institute, University Health Network（KITE研究所（大学健康网络））；The BioRobotics Institute, Scuola Superiore Sant’Anna（圣安娜高等学校生物机器人研究所）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；Sunnybrook Research Institute, Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心桑尼布鲁克研究所）；Temerty Faculty of Medicine, University of Toronto（多伦多大学特默蒂医学院）；Brain and Spinal Cord Rehabilitation Program, Toronto Rehabilitation Institute – University Health Network（大学健康网络多伦多康复研究所脑与脊髓康复项目）；Department of Physical Therapy, University of Toronto（多伦多大学物理治疗系）；Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto（多伦多大学爱德华·S·罗杰斯 Sr. 电气与计算机工程系）
Understanding-Enhanced Model Collaboration for Long-Tailed Egocentric Mistake Detection
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；School of Computer Science and Tech., University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Institute of Information Engineering, CAS（信息工程研究所，中国科学院）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
Department of Computer Science Engineering, Universidad Autónoma de Madrid（马德里自治大学计算机科学工程系）；Digital Futures Institute, Teachers College Columbia University（哥伦比亚大学教师学院数字未来研究所）
