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
Multifactor feature extraction for human movement recognition
Computer Vision and Image Understanding
Gesture recognition by stereo vision
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Real-Time gesture recognition using 3d motion history model
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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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We present a novel 3D gesture recognition scheme that combines the 3D appearance of the hand and the motion dynamics of the gesture to classify manipulative and controlling gestures. Our method does not directly track the hand. Instead, we take an object-centered approach that efficiently computes the 3D appearance using a region-based coarse stereo matching algorithm in a volume around the hand. The motion cue is captured via differentiating the appearance feature. An unsupervised learning scheme is carried out to capture the cluster structure of these feature-volumes. Then, the image sequence of a gesture is converted to a series of symbols that indicate the cluster identities of each image pair. Two schemes (forward HMMs and neural networks) are used to model the dynamics of the gestures. We implemented a real-time system and performed numerous gesture recognition experiments to analyze the performance with different combinations of the appearance and motion features. The system achieves recognition accuracy of over 96% using both the proposed appearance and the motion cues.