Multi-action recognition via stochastic modelling of optical flow and gradients

Carvajal, Johanna, Sanderson, Conrad, McCool, Christopher, & Lovell, Brian C. (2014) Multi-action recognition via stochastic modelling of optical flow and gradients. In Rahman, Ashfaqur, Deng, Jeremiah D., & Li, Jiuyoung (Eds.) Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, ACM, Gold Coast, Australia, pp. 19-24.

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Abstract

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

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ID Code: 82379
Item Type: Conference Paper
Refereed: Yes
Keywords: Human action recognition, Multi-action recognition, Segmentation, Stochastic modelling, Gaussian mixture models
DOI: 10.1145/2689746.2689748
ISBN: 9781450331593
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 ACM New York, NY, USA
Deposited On: 11 Mar 2015 00:07
Last Modified: 11 Mar 2015 23:33

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