An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
Gesture Recognition Using Temporal Template Based Trajectories
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Gesture Recognition Using 3D Appearance and Motion Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
View-independent human action recognition with Volume Motion Template on single stereo camera
Pattern Recognition Letters
3D posture representation using meshless parameterization with cylindrical virtual boundary
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Dimension reduction in 3d gesture recognition using meshless parameterization
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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In this paper, we present a novel method for real time gesture recognition with 3D Motion History Model (MHM). There are two difficult problems in gesture recognition: the camera view and the duration of gesture. First, we solved the camera view problem which is very difficult in the environment of single directional camera (e.g., monocular or stereo camera). Utilizing 3D-MHM with the disparity information, not only this problem is solved but also the reliability of recognition and the scalability of system are improved. Second, we proposed the dynamic history buffering (DHB) to solve the duration problem that comes from the variation of gesture velocity at every performing time. DHB improves the problem using magnitude of motion. We implemented a real-time system and performed gesture recognition experiments. The system using 3D-MHM achieves better results of recognition than using only 2D motion information.