Histogram-Based Training Initialisation of Hidden Markov Models for Human Action Recognition

IEEE Computer Society - Conference Publishing Services (CPS)
Publication Type:
Conference Proceeding
Proceedings: 7th IEEE International Conference on Advanced Video and Signal Based Surveillance - AVSS 2010, 2010, pp. 256 - 261
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010000980.pdf93.45 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: