Object Labelling from Human Action Recognition
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Robust recognition and segmentation of human actions using HMMs with missing observations
EURASIP Journal on Applied Signal Processing
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HMM is very well suited to model sequential patterns. This paper introduces a new parameter, the termination probability, to HMM. The new parameter provides a better initialization for the backward variable during the training and evaluation phases. This improves the discriminatory power of HMM by allowing the system to judge the input observation sequence based on where it is completed. Experimental results show the improvement achieved by this parameter.