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.
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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|Item Type:||Conference Paper|
|Keywords:||Human action recognition, Multi-action recognition, Segmentation, Stochastic modelling, Gaussian mixture models|
|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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