Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation

Asteriadis, Stylianos; Karpouzis, Kostas; Kollias, Stefanos

doi:10.1007/s11263-013-0691-3

Cited by 49 publications

(25 citation statements)

References 38 publications

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“…This information can be measured by the estimation of someone's gaze or face orientation. Several studies show how to determine the face's orientation to estimate the focus of attention in several contexts: in front of a computer screen [3], in interaction with an ECA [11,7] or a mobile robot [21,4]. In [13], the user's attention is estimated using several features: position and posture, face orientation, proximity from the system and smile detection.…”

Section: Engagement In Human-machine Interactionmentioning

confidence: 99%

Towards attention monitoring of older adults with cognitive impairment during interaction with an embodied conversational agent

Wargnier

Malaisé²,

Jacquemot

et al. 2015

2015 3rd IEEE VR International Workshop on Virtual and Augmented Assistive Technology (VAAT)

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Embodied conversational agents (ECAs) are virtual characters using verbal and non-verbal communication for Human-machine interaction. The aim of our research is to create an ECA-based user interface for assistive technologies targeting older adults with cognitive impairment. Our design methodology is a co-design living lab approach, collecting design guidelines through questionnaires, focus groups and user trials.In this paper, we report on the results of the first phase of this iterative design process. We developed Louise, a semi-automatic ECA prototype that aims to compensate, through attention monitoring, for a user's attentional disorders by performing autonomous prompting, i.e., calling the user to regain his or her attention in case he or she got distracted. We evaluated the performance of Louise with a group of experts in assistive technologies and collected their feedback. Louise's simple attention estimator is more than 80% accurate. The system got quite positive reviews from users.

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Section: Engagement In Human-machine Interactionmentioning

confidence: 99%

Towards attention monitoring of older adults with cognitive impairment during interaction with an embodied conversational agent

Wargnier

Malaisé²,

Jacquemot

et al. 2015

2015 3rd IEEE VR International Workshop on Virtual and Augmented Assistive Technology (VAAT)

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show abstract

“…Our method achieves 100% robustness and 3.9°aver-age accuracy, E m , which outperforms [7,8]. It has mean error E m similar to [25,29,31], but worse than [4,20,35,40,41]. With the use of the synthetic appearance model, the result is promising.…”

Section: Resultsmentioning

confidence: 83%

“…Recently, the cascaded regression has shown very impressive results in face alignment, such as [6,18,27,41], however, these method are developed for near frontal face between ± 45°of Yaw rotation. Asteriadis et al [4] proposed the combination of traditional tracking techniques and deep learning to provide a proficient pose tracking. Many commercial products also exist, e.g.…”

Section: Introductionmentioning

confidence: 99%

A robust framework for tracking simultaneously rigid and non-rigid face using synthesized data

Tran

Ababsa

Charbit

2015

Pattern Recognition Letters

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“…This set of scenes is then analyzed using software that calculates the visual saliency or basic attractiveness in terms of attention of each area in the scene. The areas of high and low visual saliency are mapped across the scenes, allowing for a ready assessment of which features or areas are particularly visually attractive and which are less likely to draw attention (following the kind of approach used in, e.g., McNamara et al 2014; Baluch and Itti 2015; Asteriadis et al 2014 ). This set of saliency maps, together with the corresponding images of what is visible in each moment, form the dataset from which further inferences are made.…”

Section: Aq5mentioning

confidence: 99%

An Experiment in Using Visual Attention Metrics to Think About Experience and Design Choices in Past Places

Opitz

2017

J Archaeol Method Theory

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An experiment in using visual attention metrics to think about experience and design choices in past places. Journal of Archaeological Method and Theory, 24(4), pp. 1203-1226. (doi:10.1007 This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/142948/ AbstractThe influence of the visual properties of a built space or landscape on the behaviors of people within them and the manipulation of these visual properties to cue or constrain behaviors are subjects of longstanding archaeological interest. Advances in cognitive neurosciences and a suite of improved computational modeling tools, combined with the proliferation of detailed 3D models of archaeological complexes and landscapes, offer an opportunity for new approaches to these topics based on models of lowlevel perceptual cues and visual attention. The approach described here takes aim at the question of where people will look, rather than simply what is visible, with the goal of investigating the intentions of designers of spaces and visual aspects of the experience of a place. In simple terms, our approach involves placing detailed 3D models of built spaces or landscapes into a digital environment. An individual then virtually walks through the space and what is visible at each moment is recorded in the form of a video stream, which may be broken down into a sequence of scenes. This set of scenes is then analyzed using software that calculates and maps the visual saliency of each scene and the path of focuses of attention (FOAs) over time. This set of saliency maps, raw images, and FOA paths provide the basis for further interpretation. This paper presents an initial experiment to illustrate the approach, carried out in the eastern passage at Knowth, one of the main mounds in the Brú na Bóinne in Ireland.

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Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation

Cited by 49 publications

References 38 publications

Towards attention monitoring of older adults with cognitive impairment during interaction with an embodied conversational agent

Towards attention monitoring of older adults with cognitive impairment during interaction with an embodied conversational agent

A robust framework for tracking simultaneously rigid and non-rigid face using synthesized data

An Experiment in Using Visual Attention Metrics to Think About Experience and Design Choices in Past Places

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