The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. The frequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequencies from video of a vibrating object, we can often make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video.
The world is filled with important, but visually subtle signals. A person's pulse, the breathing of an infant, the sag and sway of a bridge-these all create visual patterns, which are too difficult to see with the naked eye. We present Eulerian Video Magnification, a computational technique for visualizing subtle color and motion variations in ordinary videos by making the variations larger. It is a microscope for small changes that are hard or impossible for us to see by ourselves. In addition, these small changes can be quantitatively analyzed and used to recover sounds from vibrations in distant objects, characterize material properties, and remotely measure a person's pulse.
Visual testing, as one of the oldest methods for nondestructive testing (NDT), plays a large role in the inspection of civil infrastructure. As NDT has evolved, more quantitative techniques have emerged such as vibration analysis. New computer vision techniques for analyzing the small motions in videos, collectively called motion magnification, have been recently developed, allowing quantitative measurement of the vibration behavior of structures from videos. Video cameras offer the benefit of long range measurement and can collect a large amount of data at once because each pixel is effectively a sensor. This paper presents a video camera-based vibration measurement methodology for civil infrastructure. As a proof of concept, measurements are made of an antenna tower on top of the Green Building on the campus of the Massachusetts Institute of Technology (MIT) from a distance of over 175 m, and the resonant frequency of the antenna tower on the roof is identified with an amplitude of 0.21 mm, which was less than 1=170th of a pixel. Methods for improving the noise floor of the measurement are discussed, especially for motion compensation and the effects of video downsampling, and suggestions are given for implementing the methodology into a structural health monitoring (SHM) scheme for existing and new structures.
SignificanceHumans have difficulty seeing small motions with amplitudes below a threshold. Although there are optical techniques to visualize small static physical features (e.g., microscopes), visualization of small dynamic motions is extremely difficult. Here, we introduce a visualization tool, the motion microscope, that makes it possible to see and understand important biological and physical modes of motion. The motion microscope amplifies motions in a captured video sequence by rerendering small motions to make them large enough to see and quantifies those motions for analysis. Amplification of these tiny motions involves careful noise analysis to avoid the amplification of spurious signals. In the representative examples presented in this study, the visualizations reveal important motions that are invisible to the naked eye.
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