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Vision-Based Building Seismic Displacement Measurement by Stratification of Projective Rectification Using Lines.
Guo, Jia; Xiang, Yang; Fujita, Kohei; Takewaki, Izuru.
  • Guo J; International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai 980-8572, Japan.
  • Xiang Y; Department of Architecture and Architectural Engineering, Kyoto University, Kyoto 615-8540, Japan.
  • Fujita K; Department of Architecture and Architectural Engineering, Kyoto University, Kyoto 615-8540, Japan.
  • Takewaki I; Department of Architecture and Architectural Engineering, Kyoto University, Kyoto 615-8540, Japan.
Sensors (Basel) ; 20(20)2020 Oct 12.
Article en En | MEDLINE | ID: mdl-33053806
ABSTRACT
We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. No a priori information about the camera's parameters or only partial knowledge of the internal camera parameters is required, and geometric constraints in the world coordinate system are employed for projective rectification in this research. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. Since line features are natural and plentiful in a man-made architectural building environment, robust estimation techniques for automatic projective/affine distortion removal can be applied in a more practical way. Both simulations and real-recorded data were used to verify the effectiveness and robustness of the proposed method. We hope that the proposed method could advance the consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real-world structural health monitoring systems.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Article