Recognition-based gesture spotting in video games
Pattern Recognition Letters
Human motion estimation from a reduced marker set
I3D '06 Proceedings of the 2006 symposium on Interactive 3D graphics and games
Dance Posture Recognition Using Wide-baseline Orthogonal Stereo Cameras
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Human Action Recognition Using Multi-View Image Sequences Features
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multimodal human-computer interaction: A survey
Computer Vision and Image Understanding
Temporal Nearest End-Effectors for Real-Time Full-Body Human Actions Recognition
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Kinetic Pseudo-energy History for Human Dynamic Gestures Recognition
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Gestures are strings: efficient online gesture spotting and classification using string matching
Proceedings of the ICST 2nd international conference on Body area networks
Subject-independent natural action recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A tuned eigenspace technique for articulated motion recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Computational intelligence for cyclic gestures recognition of a partner robot
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper presents a gesture recognition method for detecting and classifying both cyclic and non-cyclic human motion patterns in real-time applications. The semantic segmentation of a constantly captured human motion data stream is a key research topic, especially if both cyclic and non-cyclic gestures are considered during the human-computer interaction. The system measures the temporal coherence of the movements being captured according to its knowledge database, and once it has a sufficient level of certainty on its observation semantics the motion pattern is labeled automatically. In this way, our recognition method is also capable of handling time-varying dynamic gestures. The effectiveness of the proposed method is demonstrated via recognition experiments with a triple-axis accelerometer and a 3D tracker used by various performers.