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Salient posture modeling based on spatio-temporal interesting points

Conference Paper


Abstract


  • An action can be represented as a sequence of salient postures. Effective modeling of the salient postures is critical for robust action recognition. This paper proposes to characterize the salient postures using a set of the spatiotemporal interesting points (STIPs). Local features are extracted at each STIP and the statistical distribution of the features for each salient posture is further modelled by a Gaussian mixture model (GMM). Experimental results have verified the effectiveness of the proposed posture model. © 2009 IEEE.

Publication Date


  • 2009

Citation


  • Wang, C. X., Liu, Y., & Li, W. (2009). Salient posture modeling based on spatio-temporal interesting points. In 2nd International Workshop on Computer Science and Engineering, WCSE 2009 Vol. 1 (pp. 604-607). doi:10.1109/WCSE.2009.741

Scopus Eid


  • 2-s2.0-77950291608

Web Of Science Accession Number


Start Page


  • 604

End Page


  • 607

Volume


  • 1

Abstract


  • An action can be represented as a sequence of salient postures. Effective modeling of the salient postures is critical for robust action recognition. This paper proposes to characterize the salient postures using a set of the spatiotemporal interesting points (STIPs). Local features are extracted at each STIP and the statistical distribution of the features for each salient posture is further modelled by a Gaussian mixture model (GMM). Experimental results have verified the effectiveness of the proposed posture model. © 2009 IEEE.

Publication Date


  • 2009

Citation


  • Wang, C. X., Liu, Y., & Li, W. (2009). Salient posture modeling based on spatio-temporal interesting points. In 2nd International Workshop on Computer Science and Engineering, WCSE 2009 Vol. 1 (pp. 604-607). doi:10.1109/WCSE.2009.741

Scopus Eid


  • 2-s2.0-77950291608

Web Of Science Accession Number


Start Page


  • 604

End Page


  • 607

Volume


  • 1