Viewpoint-independent Action Recognition Method using Depth Image

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In this paper we propose action recognition methods that use depth images for recognizing action independently on the viewpoints. We propose a method for reducing the amount of influence from changes in orientation of the people being observed, while suppressing the required quantity of training samples for dealing with the viewpoint changes between when learning and when recognizing. We first create the three-view drawings expansion by virtually changing the viewpoints within a predetermined range from the training samples when learning, and learn the weak classifier candidates respective to each viewpoint for discriminating the action categories. Then, we learn a strong classifier from these weak classifier candidates and limited number of training samples that is suitable for the viewpoint during recognition. Furthermore, we propose a method for accepting cases when the camera is too close to the people and some part of their body, i.e., an arm or leg, become visually deficient because it protrudes outside the given viewing angle in order to enlarge the region coverage for the action recognition. In this method, arbitrary motion features that are outside a given viewing angle are compensated for before discriminating the action categories by using a regression estimate that is based on a correlation between the motion features of the body parts outside the viewing angle and that of full body images. The experimental results showed that the action categories could be successfully recognized using the proposed methods even under influences from some changes in orientation or visual deficits of the people, when compared to conventional action recognition methods.

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詳細情報 詳細情報について

  • CRID
    1572824502694461184
  • NII論文ID
    110009899585
  • NII書誌ID
    AA11131797
  • ISSN
    09196072
  • 本文言語コード
    en
  • データソース種別
    • CiNii Articles

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