Structures built on sands worldwide, with shallow foundations, have experienced damage and collapse during and after earthquakes. Two phenomena triggered the collapse: the liquefaction phenomenon and the P-Δ effects. However, current research and practice do not fully understand granular soil behavior during liquefaction and P-Δ effects, as proven by the sum of investigations on physical models, constitutive models, and laboratory testing proposals about these topics. A question appears at this point: what is the relationship between excitation frequency, displacement amplitude, and the triggering of overturning? To cope with this issue, the authors propose to create a physical 1-g model composed of a single-degree-of-freedom oscillator (SDOFO) capable of transmitting cyclic loadings to the soil in rocking vibration mode. The measurement methodology was based on computer vision using OpenCV by Python, which allowed the “free movement” of the SDOFO. The authors use computer vision as a suitable way to obtain displacements and times without sensors placed directly in the physical model. According to the results, it was possible to define an inversely non-linear relationship between frequency, displacement amplitude, and the total cycles required to reach overturning for different effective grain-size (D10).