Histogram-based training initialisation of hidden Markov models for human action recognition

Publication Type:
Conference Proceeding
Citation:
Proceedings - IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, 2010, pp. 256 - 261
Issue Date:
2010-01-01
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Human action recognition is often addressed by use of latent-state models such as the hidden Markov model and similar graphical models. As such models require Expectation-Maximisation training, arbitrary choices must be made for training initialisation, with major impact on the final recognition accuracy. In this paper, we propose a histogram-based deterministic initialisation and compare it with both random and a time-based deterministic initialisations. Experiments on a human action dataset show that the accuracy of the proposed method proved higher than that of the other tested methods. © 2010 IEEE.
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