Sign language recognition using model-based tracking and a 3D Hopfield neural network
Machine Vision and Applications
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A model-based hand gesture recognition system
Machine Vision and Applications
Visual Tracking of High DOF Articulated Structures: an Application to Human Hand Tracking
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Towards 3D hand tracking using a deformable model
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Color-Based Hands Tracking System for Sign Language Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
O.G.R.E. - Open Gestures Recognition Engine
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Appearance-Guided Particle Filtering for Articulated Hand Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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In this paper we propose a non intrusive tracking system able to capture simple hand gestures in a fast and reliable way. Our device uses the images taken from a single camera to capture the 3D position and orientation of the hand of the user. In particular, the 3D position of the forefinger tip is used as a 3D marker, and the posture of the hand is used to input simple commands. The proposed approach combines several computer vision algorithms in order to exploit their strengths trying to minimize their drawbacks. The result is a real time system that is very robust against noise and cluttered backgrounds. An evaluation of the quality of the proposed approach is also presented.