Ageing structures and infrastructures need to be monitored to assess their structural health conditions and prioritize interventions to possibly extend their service life. To do this, traditional devices, such as accelerometers, are routinely adopted and are part of a consolidated state-of-the-art procedure. Nonetheless, in field applications of accelerometric measurements some difficulties may arise, related to the system energy supply, cost, accessibility. Moreover, the position of the sensors needs to be decided a priori, with some degree of engineering judgment.

Alternative techniques based on computer vision have emerged in the last decade and are becoming more and more popular as they allow to overcome most of the limitations reported above. The main advantages of these approaches rely on the possibility of high-density measurements and a relatively simple acquisition process, for which neither an expensive equipment nor advanced technical skills are mandatorily required.

In this paper we present the application of a computer vision-based technique, which combined motion magnification and statistical algorithms to extract the natural frequencies of a reinforced concrete elevated water tank, vibrating under environmental noise excitation. To this aim, several videos were recorded with a commercial reflex camera and post-processed selecting a representative area, by tracking in time either the variation of the intensity or the motion of a selected number of pixels. Computer vision-based outcomes were validated against the results provided by conventional accelerometers to discuss advantages and limitations of the proposed dynamic identification approach and identify future research challenges in this field.