Owing to the need for inexpensive, remotely applicable, and distributed sensing methodologies, we have been working on a new approach to measure natural frequencies of structures and mechanical systems from digital videos. Our earlier proposed Eulerian-based virtual visual sensors (VVS) are different than block matching-based approaches such as digital image correlation (DIC) in that we measure the change in intensity of a fixed single pixel or a patch of pixels. For our measurements we have used, besides regular DSLR cameras, commercially-available technology such as the GoPro camera, which is a popular gadget used by the athletic and adventure communities. We also investigated the use of professional high-speed cameras, with which we have been able to detect frequencies as high as 740 Hz from an impulse response test of a steel beam. A comparison between the frequencies and the signal-to-noise ratios computed from the VVS and the baseline accelerometer data showed them to be comparable. In this paper, we introduce the fundamental concept of Eulerian-based VVS and present and discuss a series of laboratory experiments on scaled structures and field tests on bridges. Finally, we discuss our most recent attempt to measure frequencies with high SNR employing targets combined with simple noise-reduction algorithms.
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