On Quadratic Interpolation of Image Cross-Correlation for Subpixel Motion Extraction

Influence of Noise in Computer-Vision-Based Measurements on Parameter Identification in Structural Dynamics

Nowadays, consumer electronics offer computer-vision-based (CV) measurements of dynamic displacements with some trade-offs between sampling frequency, resolution and low cost of the device. This study considers a consumer-grade smartphone camera based on complementary metal-oxide semiconductor (CMOS) technology and investigates the influence of its hardware limitations on the estimation of dynamic displacements, modal parameters and stiffness parameters of bolted connections in a laboratory structure. An algorithm that maximizes the zero-normalized cross-correlation function is employed to extract the dynamic displacements. The modal parameters are identified with the stochastic subspace identification method. The stiffness parameters are identified using a model-updating technique based on modal sensitivities. The results are compared with the corresponding data obtained with accelerometers and a laser distance sensor. The CV measurement allows lower-order vibration modes to be identified with a systematic (bias) error that is nearly proportional to the vibration frequency: from 2% for the first mode (9.4 Hz) to 10% for the third mode (71.4 Hz). However, the measurement errors introduced by the smartphone camera have a significantly lower influence on the values of the identified stiffness parameters than the numbers of modes and parameters taken into account. This is due to the bias-variance trade-off. The results show that consumer-grade electronics can be used as a low-cost and easy-to-use measurement tool if lower-order modes are required.

Localization of Stereovision for Measuring In-Crash Toeboard Deformation

This paper presents a technique to localize a stereo camera for in-crash toeboard deformation measurement. The proposed technique designed a sensor suite to install not only the stereo camera but also initial measurement units (IMUs) and a camera for localizing purpose. The pose of the stereo camera is recursively estimated using the measurement of IMUs and the localization camera through an extended Kalman filter. The performance of the proposed approach was first investigated in a stepwise manner and then tested in controlled environments including an actual vehicle crash test, which had successfully resulted in measuring the toeboard deformation during a crash. With the oscillation motion in the occurrence of the crash captured, the deformation of the toeboard measured by stereo cameras can be described in a fixed coordinate system.