Raman, Natraj and Maybank, Stephen J. and Zhang, Dell (2013) Action classification using a discriminative non-parametric hidden Markov model. In: Vuksanovic, B. and Zhou, J. and Verikas, A. (eds.) Sixth International Conference on Machine Vision (ICMV 2013). SPIE Proceedings 9067. International Society for Optics and Photonics. ISBN 9780819499967.
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Abstract
We classify human actions occurring in videos, using the skeletal joint positions extracted from a depth image sequence as features. Each action class is represented by a non-parametric Hidden Markov Model (NP-HMM) and the model parameters are learnt in a discriminative way. Specifically, we use a Bayesian framework based on Hierarchical Dirichlet Process (HDP) to automatically infer the cardinality of hidden states and formulate a discriminative function based on distance between Gaussian distributions to improve classification performance. We use elliptical slice sampling to efficiently sample parameters from the complex posterior distribution induced by our discriminative likelihood function. We illustrate our classification results for action class models trained using this technique.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | Sixth International Conference on Machine Vision (ICMV 2013) - April 16th 2013, London, UK Copyright 2013 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. |
| Keyword(s) / Subject(s): | action classification, depth image, HDP-HMM, discriminative, elliptical slice sampling |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | Professor Stephen Maybank |
| Date Deposited: | 06 Mar 2014 09:10 |
| Last Modified: | 09 Aug 2023 12:34 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/9291 |
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