Machine Vision and Applications

, Volume 25, Issue 4, pp 1007–1018 | Cite as

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

Original Paper

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.

Keywords

Silhouette analysis Human action recognition Gait recognition Manifold learning  Hausdorff distance 

Notes

Acknowledgments

This work was supported by the National Science Foundation of China (No. 61301269 and No. 61201271), the Research Fund for the Doctoral Program of Higher Education (No. 20100185120021), the Science and Technology Cooperation Program with the Academy of China and Sichuan Province (No. 2012JZ0001).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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