Natural Interface Using Pointing Behavior for Human–Robot Gestural Interaction

Sato, Eri; Yamaguchi, Toru; Harashima, Fumio

doi:10.1109/tie.2007.892728

Cited by 115 publications

(39 citation statements)

References 8 publications

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“…[33]- [35]) and estimating the referred-to target has been addressed by several authors in recent years, aiming at applications in robotics (e.g. [36]- [39]), smart environments (e.g. [40]) and wearable visual interfaces (e.g.…”

Section: Related Workmentioning

confidence: 99%

Saliency-based identification and recognition of pointed-at objects

Schauerte

Richarz

Fink

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-When persons interact, non-verbal cues are used to direct the attention of persons towards objects of interest. Achieving joint attention this way is an important aspect of natural communication. Most importantly, it allows to couple verbal descriptions with the visual appearance of objects, if the referred-to object is non-verbally indicated. In this contribution, we present a system that utilizes bottom-up saliency and pointing gestures to efficiently identify pointed-at objects. Furthermore, the system focuses the visual attention by steering a pan-tilt-zoom camera towards the object of interest and thus provides a suitable model-view for SIFT-based recognition and learning. We demonstrate the practical applicability of the proposed system through experimental evaluation in different environments with multiple pointers and objects.

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Section: Related Workmentioning

confidence: 99%

Saliency-based identification and recognition of pointed-at objects

Schauerte

Richarz

Fink

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Sato [9] uses pointing gesture to give command to service robot in virtual space. In her research, the pointing direction is estimated by using marker at user's cap and hand glove.…”

Section: A Related Workmentioning

confidence: 99%

Pointing Angle Estimation for Human-Robot Interface

Nor¹,

Maeda²,

Mizukawa³

2013

JACN

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Abstract-In this paper, we propose an approach to track and estimate user pointing direction in 3D Space. In the area of human-robot interaction, user communicates with service robot in their daily life activities to give commands and execute the given task accordingly. Therefore, the ability of user to gives command to service robot naturally can provide an interactive user interface system for real 3D space environment. For this purpose, we aim to perform pointing gesture tracking and after that estimate the user's pointing direction. Our method of pointing direction estimation is based on 3D orientation of hand and shoulder center of user. We make comparison with our previous method to find the best hypothesis. Experimental results show the angular error for the estimation of pointing direction is successfully improved from our previous method. As a result, our natural user interface system can manipulate 3D objects in living room environment thus providing intuitive robotic service for human robot interaction.Index Terms-Human-robot interaction, pointing gesture, user tracking.

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“…[22]), and robots (e.g. [21,26,35,42,45]). Unfortunately, most of these systems require that the objects present in the scene were already detected, segmented, recognized, categorized and/or their attributes identified.…”

Section: Pointingmentioning

confidence: 99%

Focusing computational visual attention in multi-modal human-robot interaction

Schauerte

Fink

2010

International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction

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Identifying verbally and non-verbally referred-to objects is an important aspect of human-robot interaction. Most importantly, it is essential to achieve a joint focus of attention and, thus, a natural interaction behavior. In this contribution, we introduce a saliencybased model that reflects how multi-modal referring acts influence the visual search, i.e. the task to find a specific object in a scene. Therefore, we combine positional information obtained from pointing gestures with contextual knowledge about the visual appearance of the referred-to object obtained from language. The available information is then integrated into a biologically-motivated saliency model that forms the basis for visual search. We prove the feasibility of the proposed approach by presenting the results of an experimental evaluation.

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Natural Interface Using Pointing Behavior for Human–Robot Gestural Interaction

Cited by 115 publications

References 8 publications

Saliency-based identification and recognition of pointed-at objects

Saliency-based identification and recognition of pointed-at objects

Pointing Angle Estimation for Human-Robot Interface

Focusing computational visual attention in multi-modal human-robot interaction

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