Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning-based dynamic coupling of discrete and continuous trackers
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Automated detection of unusual events on stairs
Image and Vision Computing
3-D hand trajectory recognition for signing exact English
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
American sign language recognition with the kinect
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
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Human motion recognition has many important applications, such as improved human-computer interaction and surveillance. A big problem that plagues this research area is that human movements can be very complex. Managing this complexity is difficult. We turn to American sign language (ASL) recognition to identify general methods that reduce the complexity of human motion recognition. We present a framework for continuous 3D ASL recognition based on linguistic principles, especially the phonology of ASL. This framework is based on parallel hidden Markov models (HMMs), which are able to capture both the sequential and the simultaneous aspects of the language. Each HMM is based on a single phoneme of ASL. Because the phonemes are limited in number, as opposed to the virtually unlimited number of signs that can be composed from them, we expect this framework to scale well to larger applications. We then demonstrate the general applicability of this framework to other human motion recognition tasks by extending it to gait recognition.