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  • 1.
    Alexopoulou, Sofia
    et al.
    Örebro University, School of Humanities, Education and Social Sciences.
    Fart, Frida
    Örebro University, School of Medical Sciences.
    Jonsson, Ann-Sofie
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Karni, Liran
    Örebro University, Örebro University School of Business.
    Kenalemang, Lame Maatla
    Örebro University, School of Humanities, Education and Social Sciences.
    Krishna, Sai
    Örebro University, School of Science and Technology.
    Lindblad, Katarina
    Örebro University, School of Music, Theatre and Art.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lundin, Elin
    Örebro University, School of Health Sciences.
    Samzelius, Hanna
    Örebro University, School of Humanities, Education and Social Sciences.
    Schoultz, Magnus
    Örebro University, School of Humanities, Education and Social Sciences.
    Spang, Lisa
    Örebro University, School of Health Sciences.
    Söderman, Annika
    Örebro University, School of Health Sciences.
    Tarum, Janelle
    Örebro University, School of Health Sciences.
    Tsertsidis, Antonios
    Örebro University, Örebro University School of Business.
    Widell, Bettina
    Örebro University, School of Humanities, Education and Social Sciences.
    Nilsson, Kerstin (Editor)
    Örebro University, School of Medical Sciences.
    Successful ageing in an interdisciplinary context: popular science presentations2018Book (Other (popular science, discussion, etc.))
  • 2.
    Krishna, Sai
    Örebro University, School of Science and Technology.
    Join the Group Formations using Social Cues in Social Robots2018In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '18), New York: Association for Computing Machinery (ACM), 2018, p. 1766-1767Conference paper (Refereed)
    Abstract [en]

    This work investigates how agents can spatially orient themselves into formations which provide good conditions for enabling social interaction. To achieve this, we are using socio-psychological notion, F-formation in our project and based on this concept, we detect positions of other agents in a scene to find the optimum placement. Using both simulation and real robotic systems, the system aims to achieve a functionality which enables an agent to autonomously place itself within a group.

  • 3.
    Krishna, Sai
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Repsilber, Dirk
    Örebro University, School of Medical Sciences.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A Novel Method for Estimating Distances from a Robot to Humans Using Egocentric RGB Camera2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 14, article id E3142Article in journal (Refereed)
    Abstract [en]

    Estimating distances between people and robots plays a crucial role in understanding social Human-Robot Interaction (HRI) from an egocentric view. It is a key step if robots should engage in social interactions, and to collaborate with people as part of human-robot teams. For distance estimation between a person and a robot, different sensors can be employed, and the number of challenges to be addressed by the distance estimation methods rise with the simplicity of the technology of a sensor. In the case of estimating distances using individual images from a single camera in a egocentric position, it is often required that individuals in the scene are facing the camera, do not occlude each other, and are fairly visible so specific facial or body features can be identified. In this paper, we propose a novel method for estimating distances between a robot and people using single images from a single egocentric camera. The method is based on previously proven 2D pose estimation, which allows partial occlusions, cluttered background, and relatively low resolution. The method estimates distance with respect to the camera based on the Euclidean distance between ear and torso of people in the image plane. Ear and torso characteristic points has been selected based on their relatively high visibility regardless of a person orientation and a certain degree of uniformity with regard to the age and gender. Experimental validation demonstrates effectiveness of the proposed method.

  • 4.
    Krishna, Sai
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards a Method to Detect F-formations in Real-Time to Enable Social Robots to Join Groups2017In: Towards a Method to Detect F-formations in Real-Time to Enable Social Robots to Join Groups, Umeå, Sweden: Umeå University , 2017Conference paper (Refereed)
    Abstract [en]

    In this paper, we extend an algorithm to detect constraint based F-formations for a telepresence robot and also consider the situation when the robot is in motion. The proposed algorithm is computationally inexpensive, uses an egocentric (first-person) vision, low memory, low quality vision settings and also works in real time which is explicitly designed for a mobile robot. The proposed approach is a first step advancing in the direction of automatically detecting F-formations for the robotics community.

  • 5.
    Krishna, Sai
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Mälardalen University.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Estimating Optimal Placement for a Robot in Social Group Interaction2019In: RO-MAN, IEEE, 2019, Vol. 28, article id 0172Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a model to propose anoptimal placement for a robot in a social group interaction. Ourmodel estimates the O-space according to the F-formation theory. The method automatically calculates a suitable placementfor the robot. An evaluation of the method has been performedby conducting an experiment where participants stand in differ-ent formations and a robot is teleoperated to join the group. Inone condition, the operator positions the robot according to thespecified location given by our algorithm. In another condition,operators have the freedom to position the robot according totheir personal choice. Follow-up questionnaires were performedto determine which of the placements were preferred by theparticipants. The results indicate that the proposed methodfor automatic placement of the robot is supported from theparticipants. The contribution of this work resides in a novelmethod to automatically estimate the best placement of therobot, as well as the results from user experiments to verify thequality of this method. These results suggest that teleoperatedrobots such as mobile robot telepresence systems could benefitfrom tools that assist operators in placing the robot in groupsin a socially accepted manner.

  • 6.
    Krishna, Sai
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    F-Formations for Social Interaction in Simulation Using Virtual Agents and Mobile Robotic Telepresence Systems2019In: Multimodal Technologies and Interaction, ISSN 2414-4088, Vol. 3, no 4, article id 69Article in journal (Refereed)
    Abstract [en]

    F-formations are a set of possible patterns in which groups of people tend to spatially organize themselves while engaging in social interactions. In this paper, we study the behavior of teleoperators of mobile robotic telepresence systems to determine whether they adhere to spatial formations when navigating to groups. This work uses a simulated environment in which teleoperators are requested to navigate to different groups of virtual agents. The simulated environment represents a conference lobby scenario where multiple groups of Virtual Agents with varying group sizes are placed in different spatial formations. The task requires teleoperators to navigate a robot to join each group using an egocentric-perspective camera. In a second phase, teleoperators are allowed to evaluate their own performance by reviewing how they navigated the robot from an exocentric perspective. The two important outcomes from this study are, firstly, teleoperators inherently respect F-formations even when operating a mobile robotic telepresence system. Secondly, teleoperators prefer additional support in order to correctly navigate the robot into a preferred position that adheres to F-formations.

  • 7.
    Krishna, Sai
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Robotics for Successful Ageing2018In: Successful ageing in an interdisciplinary context: popular science presentations / [ed] Eleonor Kristoffersson & Kerstin Nilsson, Örebro, Sweden: Örebro University , 2018, p. 29-35Chapter in book (Other (popular science, discussion, etc.))
    Abstract [en]

    The main idea of the ongoing research is to use robotics to create new opportunities to help older people to remain alone in their apartments which can beachieved by using robots as an interacting tool between the elderly and theirfamily members or doctors. This can be done by building a system (software)for Mobile Robots to work autonomously (self-driving) and semi-autono-mously (controlled by the user) when necessary, depending on the situationand the surroundings. This system is integrated with social cues, particularlyproxemics, to know and understand human space, which is very importantfor social interaction. In conclusion, we are interested in having a Socially Intelligent Robot, which could use the social cues, proxemics, to have a natural interaction with people in groups.

  • 8.
    Terzic, Kasim
    et al.
    School of Computer Science, University of St Andrews, St Andrews, United Kingdom; Department of Electronic Engineering and Computer Science, University of the Algarve, Faro, Portugal.
    Krishna, Sai
    Örebro University, School of Science and Technology. Department of Electronic Engineering and Computer Science, University of the Algarve, Faro, Portugal.
    du Buf, J. M. H.
    Department of Electronic Engineering and Computer Science, University of the Algarve, Faro, Portugal.
    Texture features for object salience2017In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 67, p. 43-51Article in journal (Refereed)
    Abstract [en]

    Although texture is important for many vision-related tasks, it is not used in most salience models. As a consequence, there are images where all existing salience algorithms fail. We introduce a novel set of texture features built on top of a fast model of complex cells in striate cortex, i.e., visual area V1. The texture at each position is characterised by the two-dimensional local power spectrum obtained from Gabor filters which are tuned to many scales and orientations. We then apply a parametric model and describe the local spectrum by the combination of two one-dimensional Gaussian approximations: the scale and orientation distributions. The scale distribution indicates whether the texture has a dominant frequency and what frequency it is. Likewise, the orientation distribution attests the degree of anisotropy. We evaluate the features in combination with the state-of-the-art VOCUS2 salience algorithm. We found that using our novel texture features in addition to colour improves AUC by 3.8% on the PASCAL-S dataset when compared to the colour-only baseline, and by 62% on a novel texture-based dataset.

1 - 8 of 8
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