Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Visual recognition of pointing gestures for human-robot interaction
Image and Vision Computing
Hand gesture recognition based on dynamic Bayesian network framework
Pattern Recognition
Combining RGB and ToF cameras for real-time 3D hand gesture interaction
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
| Hi-index | 0.00 |
Gesture recognition plays an important role in Human Computer Interaction (HCI) but in most HCI systems, the user is limited to use only one hand or two hands under optimal conditions. Challenges are for instance non-homogeneous backgrounds, hand-hand or hand-face overlapping and brightness modifications. In this research, we have proposed a novel approach that solves the ambiguities occurred due to the hand overlapping robustly based on multi-hypotheses object association. This multi-hypotheses object association builds the basis for the tracking in which the hand trajectories are computed and this leads us to extract the features. The gesture recognition phase takes the extracted features and classifies them through Hidden Markov Model (HMM).