Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Bio-inspired Connectionist Architecture for Visual Detection and Refinement of Shapes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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An appraisal of human motions and particular motion phases is essential for a good interaction between a human and a humanoid robot. We present a new method for the analysis of human motions and the classification of motion phases. The method allows an automatic composition of a motion model for a complex motion from several elementary models. The elementary models can be retrieved from a motion catalogue according to the requirements of a current motion processing task. The method is based on the analysis of the hidden states in a complex HMM and considers the context of all elementary phases in an entire motion sequence. The analysis of motion phases with the new model is computationally more efficient and yields better recognition rates than conventional motion analysis with HMMs and winner-takes-all strategy