Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
DigitEyes: Vision-Based Human Hand Tracking
DigitEyes: Vision-Based Human Hand Tracking
Fast 2D Hand Tracking with Flocks of Features and Multi-Cue Integration
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Smart particle filtering for 3D hand tracking
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Large-Vocabulary Continuous Sign Language Recognition Based on Transition-Movement Models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
In this paper, a robust hand tracking algorithm is proposed for 3D sign language recognition application. The most challenging problems in hand tracking are the complex background, various illumination and articulated hand motion. In our work, based on the color and depth information simultaneously, two hands are well tracked by similarity optimization framework. However, in the fusion procedure, one important problem must be considered is that the data from the color and the depth channels are not always synchronous for the hardware reason. A two-layer difference comparison scheme is presented to determine whether the color and depth data are consistent. According to this consistency determination, the depth data can be used confidently or be deserted. Experiments on 300 sign language videos convincingly show the accuracy and robustness of the proposed hand tracking method. The visualized results also show the good performance even for the complex two-hand overlapping situations.