Silhouette analysis for human action recognition based on maximum spatio-temporal dissimilarity embedding

  • Cheng J
  • Liu H
  • Li H
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Abstract

In this paper, we present a human action recognition method for human
silhouette sequences. Inspired by the locality preserving projection and
its variants, a novel manifold embedding method, maximum spatio-temporal
dissimilarity embedding, is proposed to embed each action frame into a
manifold, where frames from different action classes can be well
separated. Unlike existing methods that incorporate both inter-class and
intra-class information in the embedding process, our proposed method
focuses on maximizing distances between frames that are similar in
appearance but are from different classes and takes the temporal
information into consideration. A variant of Hausdorff distance is
introduced for frame and sequence classifications. Extensive
experimental results and comparison with state-of-the-art methods
demonstrate the effectiveness and robustness of the proposed method for
human action silhouette analysis.

Author-supplied keywords

  • Gait recognition
  • Hausdorff distance
  • Human action recognition
  • Manifold learning
  • Silhouette analysis

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Authors

  • Jian Cheng

  • Haijun Liu

  • Hongsheng Li

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