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Proceedings Paper

Comparative study of methods for recognition of an unknown person's action from a video sequence
Author(s): Takayuki Hori; Jun Ohya; Jun Kurumisawa
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Paper Abstract

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.

Paper Details

Date Published: 11 February 2009
PDF: 10 pages
Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 72450V (11 February 2009); doi: 10.1117/12.805745
Show Author Affiliations
Takayuki Hori, Waseda Univ. (Japan)
Jun Ohya, Waseda Univ. (Japan)
Jun Kurumisawa, Chiba Univ. of Commerce (Japan)


Published in SPIE Proceedings Vol. 7245:
Image Processing: Algorithms and Systems VII
Nasser M. Nasrabadi; Syed A. Rizvi; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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