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Motion Magnification for Urban Buildings

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Critical Information Infrastructures Security (CRITIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10707))

Abstract

Vibration monitoring of buildings in the urban environment is a relevant issue for health survey and early damaging detection in sustainable and enhanced resilient cities. To this end, we explore the potentialities of vibration monitoring by motion magnification analysis that acts like a microscope for motion in video sequences, but affecting only some groups of pixels. The magnified motion is a new discipline in the field of the analysis of mechanical structures and buildings. It was developed from the analysis of small motions in videos. The motion magnification uses the spatial resolution of the video camera to extract physical properties from images to make inferences about the dynamical behavior of the observed object. The methodology does not rely on optics, but on algorithms capable to amplify only the tiny changes in the video frames, while the large ones remain. Recently, a number of experiments conducted on simple geometries like rods and other small objects, as well as on bridges, showed the reliability of this methodology compared to accelerometers and laser vibrometers. The extension of magnified motion to monitoring of buildings would provide many advantages: a clear, simple, immediate and intuitive diagnosis of the structure, flexibility, predictive potentialities, ease of use, low costs. But also some difficulties still do exist and are discussed. Here we give an introduction to the methodology and some case-studies, both in laboratory and in the real-world (see videos from the link): applications to the short-term urban resilience is straightforward.

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References

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Correspondence to Vincenzo Fioriti .

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Fioriti, V., Roselli, I., Tati, A., Romano, R., De Canio, G. (2018). Motion Magnification for Urban Buildings. In: D'Agostino, G., Scala, A. (eds) Critical Information Infrastructures Security. CRITIS 2017. Lecture Notes in Computer Science(), vol 10707. Springer, Cham. https://doi.org/10.1007/978-3-319-99843-5_23

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  • DOI: https://doi.org/10.1007/978-3-319-99843-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99842-8

  • Online ISBN: 978-3-319-99843-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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