21,893 research outputs found
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Human-Robot Motion: Taking Attention into Account
Mobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of the environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM.Les robots compagnons mobiles sont des robots de service conçus pour partager et se dĂ©placer dans notre espace de vie. Pour de tels robots, la mobilitĂ© est essentielle et leur coexistence avec des humains ajoute de nouveaux aspects Ă ce sujet de recherche: le premier est d'obtenir un mouvement appropriĂ© et le second est l'interaction au travers du mouvement. On regroupe ces deux aspects sous le terme Human-Robot Motion (HRM) en rĂ©fĂ©rence Ă Human-Robot Interaction. L'objectif Ă long terme est la conception de robots compagnons dont le mouvement, tout en restant sans danger, est jugĂ© appropriĂ© d'un point de vue humain. Ceci est la clĂ© de l'acceptation de tels systĂšmes dans notre vie quotidienne. L'objectif de ce papier est d'explorer comment le concept psychologique d'attention peut ĂȘtre prix en compte dans HRM. A cette fin, nous proposons un concept nouveau de champ attentionnel qui peut ĂȘtre vu comme un prĂ©dicteur attentionnel. Nos travaux se basent sur un modĂšle existant qui quantifie l'attention humaine et fournit une matrice attentionnelle qui dĂ©crit la distribution des ressources attentionnelles de chaque personne entre les diffĂ©rents Ă©lĂ©ments, personnes et objets de son environnement. Le calcul du champ attentionnel introduit dĂ©coule de cette matrice attentionnelle. En considĂ©rant diffĂ©rents scĂ©narios d'Ă©tude, on montre comment la matrice et le champ attentionnel(le) peuvent ĂȘtre utilisĂ©s en HRM
Averting Robot Eyes
Home robots will cause privacy harms. At the same time, they can provide beneficial servicesâas long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms.
We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology
- âŠ