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Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2016)

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Abstract

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes.

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Acknowledgements

NICTA is funded by the Australian Government via the Department of Communications, and the Australian Research Council via the ICT Centre of Excellence program.

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Authors and Affiliations

  1. University of Queensland, Brisbane, Australia

    Johanna Carvajal, Arnold Wiliem, Brian Lovell & Conrad Sanderson

  2. Queensland University of Technology, Brisbane, Australia

    Chris McCool

  3. NICTA, Brisbane, Australia

    Johanna Carvajal & Conrad Sanderson

  4. Data61, CSIRO, Canberra, Australia

    Conrad Sanderson

Authors
  1. Johanna Carvajal
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  2. Arnold Wiliem
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  3. Chris McCool
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  4. Brian Lovell
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  5. Conrad Sanderson
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Corresponding author

Correspondence to Conrad Sanderson .

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Editors and Affiliations

  1. New Mexico State University , Las Cruces, New Mexico, USA

    Huiping Cao

  2. University of Technology Sydney , Sydney, New South Wales, Australia

    Jinyan Li

  3. Massey University , Auckland, New Zealand

    Ruili Wang

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Carvajal, J., Wiliem, A., McCool, C., Lovell, B., Sanderson, C. (2016). Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-42996-0_8

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