Multi-view Gymnastic Activity Recognition with Fused HMM

  • Ying Wang
  • Kaiqi Huang
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

More and more researchers focus their studies on multi-view activity recognition, because a fixed view could not provide enough information for recognition. In this paper, we use multi-view features to recognize six kinds of gymnastic activities. Firstly, shape-based features are extracted from two orthogonal cameras in the form of \(\Re\) transform. Then a multi-view approach based on Fused HMM is proposed to combine different features for similar gymnastic activity recognition. Compared with other activity models, our method achieves better performance even in the case of frame loss.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ying Wang
    • 1
  • Kaiqi Huang
    • 1
  • Tieniu Tan
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 

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