IEEE Computer Graphics and Applications
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Region Tracking With Parametric Models of Geometry and Illumination
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Detecting, Tracking, and Interpretation of a Pointing Gesture by an Overhead View Camera
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Detection and Estimation of Pointing Gestures in Dense Disparity Maps
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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Visual recognition of pointing gestures for human-robot interaction
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Propagation of Pixel Hypotheses for Multiple Objects Tracking
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Tracking of facial features to support human-robot interaction
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Tracking of human hands and faces through probabilistic fusion of multiple visual cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Real-time 3D pointing gesture recognition for mobile robots with cascade HMM and particle filter
Image and Vision Computing
Hand Pointing Estimation for Human Computer Interaction Based on Two Orthogonal-Views
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Single camera pointing gesture recognition for interaction in edutainment applications
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Real-time pointing gesture recognition for an immersive environment
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Two-handed gesture recognition and fusion with speech to command a robot
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Vision-Based interpretation of hand gestures for remote control of a computer mouse
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper we address an important issue in human-robot interaction, that of accurately deriving pointing information from a corresponding gesture. Based on the fact that in most applications it is the pointed object rather than the actual pointing direction which is important, we formulate a novel approach which takes into account prior information about the location of possible pointed targets. To decide about the pointed object, the proposed approach uses the Dempster-Shafer theory of evidence to fuse information from two different input streams: head pose, estimated by visually tracking the off-plane rotations of the face, and hand pointing orientation. Detailed experimental results are presented that validate the effectiveness of the method in realistic application setups.