Robust dimensionality reduction for human action recognition

Oscar Perez Concha, Richard Yi Da Xu, Massimo Piccardi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)


Human action recognition can be approached by combining an action-discriminative feature set with a classifier. However, the dimensionality of typical feature sets joint with that of the time dimension often leads to a curse-of-dimensionality situation. Moreover, the measurement of the feature set is subject to sometime severe errors. This paper presents an approach to human action recognition based on robust dimensionality reduction. The observation probabilities of hidden Markov models (HMM) are modelled by mixtures of probabilistic principal components analyzers and mixtures of t-distribution sub-spaces, and compared with conventional Gaussian mixture models. Experimental results on two datasets show that dimensionality reduction helps improve the classification accuracy and that the heavier-tailed t-distribution can help reduce the impact of outliers generated by segmentation errors.

Original languageEnglish
Title of host publicationProceedings - 2010 Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2010
Place of PublicationSydney, Australia
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9780769542713
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2010 - Sydney, NSW, Australia
Duration: 1 Dec 20103 Dec 2010


OtherInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2010
CitySydney, NSW


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