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Charting-based subspace learning for video-based human action classification

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Abstract

We use charting, a non-linear dimensionality reduction algorithm, for articulated human motion classification in multi-view sequences or 3D data. Charting estimates automatically the intrinsic dimensionality of the latent subspace and preserves local neighbourhood and global structure of high-dimensional data. We classify human actions sub-sequences of varying lengths of skeletal poses, adopting a multi-layered subspace classification scheme with layered pruning and search. The sub-sequences of varying lengths of skeletal poses can be extracted using either markerless articulated tracking algorithms or markerless motion capture systems. We present a qualitative and quantitative comparison of single-subspace and multiple-subspace classification algorithms. We also identify the minimum length of action skeletal poses, required for accurate classification, using competing classification systems as the baseline. We test our motion classification framework on HumanEva, CMU, HDM05 and ACCAD mocap datasets and achieve similar or better classification accuracy than various comparable systems.

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Authors and Affiliations

  1. IAS group, University of Amsterdam, Amsterdam, Netherlands

    Vijay John

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

    Emanuele Trucco

Authors
  1. Vijay John
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  2. Emanuele Trucco
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Correspondence to Vijay John.

Additional information

This article is based on the first author’s Ph.D research at School of Computing, University of Dundee.

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John, V., Trucco, E. Charting-based subspace learning for video-based human action classification. Machine Vision and Applications 25, 119–132 (2014). https://doi.org/10.1007/s00138-013-0508-y

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