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Multi-view transition HMMs based view-invariant human action recognition method

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Abstract

View-invariant human action recognition is a challenging research topic in computer vision. Hidden Markov Models(HMM) and their extensions have been widely used for view-invariant action recognition. However those methods are usually according to a large parameter space, requiring amounts of training data and with low classification accuracies for real application. A novel graphical structure based on HMM with multi-view transition is proposed to model the human action with viewpoint changing. The model consists of multiple sub action models, which correspond to the traditional HMM utilized to model the human action in a particular rotation viewpoint space. In the training process, the novel model can be built by connecting the sub action models between adjacent viewpoint spaces. In the recognition process, action with unknown viewpoint is recognized by using improved forward algorithm. The proposed model can not only simplify the model training process by decomposing the parameter space into multiple sub-spaces, but also improve the performance the algorithm by constraining the possible viewpoint changing. Experiment results on IXMAS dataset demonstrated that the proposed model obtains better performance than other recent view-invariant action recognition method.

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Acknowledgments

The Project supported by the National Natural Science Foundation of China No. 61103123 and the Program for Liaoning Excellent Talents in University (No. LJQ2014018).

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Authors and Affiliations

  1. School of Automation, Shenyang Aerospace University, Shenyang, China

    Xiaofei Ji, Ce Wang & Changhui Wang

  2. School of Computing, University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

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  1. Xiaofei Ji
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  2. Zhaojie Ju
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  3. Ce Wang
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  4. Changhui Wang
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Correspondence to Xiaofei Ji.

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Ji, X., Ju, Z., Wang, C. et al. Multi-view transition HMMs based view-invariant human action recognition method. Multimed Tools Appl 75, 11847–11864 (2016). https://doi.org/10.1007/s11042-015-2661-y

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  • DOI: https://doi.org/10.1007/s11042-015-2661-y

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