Arbitrary view action recognition via transfer dictionary learning on synthetic training data

Zhang, Jingtian, Zhang, Lining, Shum, Hubert P. H. and Shao, Ling (2016) Arbitrary view action recognition via transfer dictionary learning on synthetic training data. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). The Institute of Electrical and Electronics Engineers (IEEE), SWE, pp. 1678-1684.

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Abstract

Human action recognition is an important problem in robotic vision. Traditional recognition algorithms usually require the knowledge of view angle, which is not always available in robotic applications such as active vision. In this paper, we propose a new framework to recognize actions with arbitrary views. A main feature of our algorithm is that view-invariance is learned from synthetic 2D and 3D training data using transfer dictionary learning. This guarantees the availability of training data, and removes the hassle of obtaining real world video in specific viewing angles. The result of the process is a dictionary that can project real world 2D video into a view-invariant sparse representation. This facilitates the training of a view-invariant classifier. Experimental results on the IXMAS and N-UCLA datasets show significant improvements over existing algorithms.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	Pure Connector
Date Deposited:	07 Feb 2017 02:43
Last Modified:	22 Oct 2022 00:00
URI:	https://ueaeprints.uea.ac.uk/id/eprint/62341
DOI:	10.1109/ICRA.2016.7487309

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