Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Discontinuous Motion Using Bayesian Inference
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
High Performance Real-Time Gesture Recognition Using Hidden Markov Models
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Extraction and Classification of Visual Motion Patterns for Hand Gesture Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Theoretical Consideration of Pattern Space Trajectory for Gesture Spotting Recognition
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
View-Based Interpretation of Real-Time Optical Flow for Gesture Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking of deformable human hand in real time as continuous input for gesture-based interaction
Proceedings of the 12th international conference on Intelligent user interfaces
The Journal of Machine Learning Research
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We present a system for tracking the hands of a user in a frontal camera view for gesture recognition purposes. The system uses multiple cues, incorporates tracing and prediction algorithms, and applies probabilistic inference to determine the trajectories of the hands reliably even in case of hand-face overlap. A method for assessing tracking quality is also introduced. Tests were performed with image sequences of 152 signs from German Sign Language, which have been segmented manually beforehand to offer a basis for quantitative evaluation. A hit rate of 81.1% was achieved on this material.