Toward a vision-based hand gesture interface
VRST '94 Proceedings of the conference on Virtual reality software and technology
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated gesture segmentation from dance sequences
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
2D and 3d full-body gesture database for analyzing daily human gestures
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Gesture spotting for low-resolution sports video annotation
Pattern Recognition
The recognition and comprehension of hand gestures: a review and research agenda
ZiF'06 Proceedings of the Embodied communication in humans and machines, 2nd ZiF research group international conference on Modeling communication with robots and virtual humans
Automated recognition of sequential patterns in captured motion streams
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Robust player gesture spotting and recognition in low-resolution sports video
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Gestures are expressive and meaningful body motions used in daily life as a means of communication so many researchers have aimed to provide natural ways for human-computer interaction through automatic gesture recognition. However, most of researches on recognition of actions focused mainly on sign gesture. It is difficult to directly extend to recognize whole body gesture. Moreover, previous approaches used manually segmented image sequences. This paper focuses on recognition and segmentation of whole body gestures, such as walking, running, and sitting. We introduce the gesture spotting algorithm that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture pattern. In the proposed gesture spotting algorithm, the likelihood of non-gesture Hidden Markov Models(HMM) can be used as an adaptive threshold for selecting proper gestures. The proposed method has been tested with a 3D motion capture data, which are generated with gesture eigen vector and Gaussian random variables for adequate variation. It achieves an average recognition rate of 98.3% with six consecutive gestures which contains non-gestures.