Viewpoint insensitive actions recognition using hidden conditional random fields

Authors:
Xiaofei Ji;Honghai Liu;Yibo Li
Affiliations:
School of Creative Technologies, University of Portsmout and School of Automation, Shenyang Institute of Aeronautical Engineering;School of Creative Technologies, University of Portsmout;School of Automation, Shenyang Institute of Aeronautical Engineering
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

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Abstract

The viewpoint issue has been one of the bottlenecks for research development and practical implementation of human motion analysis. In this paper, we introduce a new method, e.g., hidden conditional random fields(HCRFs) to achieve viewpoint insensitive human action recognition. The HCRF model can relax the independence assumption of the generative models. So it is very suitable to model the human actions fromdifferent actors and different viewpoints.Experiment results on a public dataset demonstrate the effectiveness and robustness of our method.