Action Recognition with Global Features

  • Arash Mokhber
  • Catherine Achard
  • Xingtai Qu
  • Maurice Milgram
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3766)


In this study, a new method allowing recognizing and segmenting everyday life actions is proposed. Only one camera is utilized without calibration. Viewpoint invariance is obtained by several acquisitions of the same action. To enhance robustness, each sequence is characterized globally: a detection of moving areas is first computed on each image. All these binary points form a volume in the three-dimensional (3D) space (x,y,t). This volume is characterized by its geometric 3D moments. Action recognition is then carried out by computing the Mahalanobis distance between the vector of features of the action to be recognized and those of the reference database. Results, which validate the suggested approach, are presented on a base of 1662 sequences performed by several persons and categorized in eight actions. An extension of the method for the segmentation of sequences with several actions is also proposed.


Hide Markov Model Recognition Rate Action Recognition Gesture Recognition Infinite Impulse Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Arash Mokhber
    • 1
  • Catherine Achard
    • 1
  • Xingtai Qu
    • 1
  • Maurice Milgram
    • 1
  1. 1.Laboratoire des Instruments et Systèmes d’Ile de France (LISIF)Université Pierre et Marie CurieParis cedex 05

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