Vision-Based Faint Vibration Extraction Using Singular Value Decomposition

• Published in: Mathematical problems in engineering Vol. 2015; no. 2015; pp. 1 - 14
• Main Authors: Lei, Xiujun, Zhu, Changan, Guo, Jie
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc
• Subjects: Algorithms; Barriers; Decomposition; Displacement measurement; Frames; Frames (data processing); High speed rail; High speed trains; Image processing; Kalman filters; Linear algebra; Mathematical analysis; Mathematical problems; Railway engineering; Remote sensors; Singular value decomposition; Sound; Vibration; Vibration measurement; China
• DOI: 10.1155/2015/306865
• ISSN: 1024-123X

Vibration measurement is important for understanding the behavior of engineering structures. Unlike conventional contact-type measurements, vision-based methodologies have attracted a great deal of attention because of the advantages of remote measurement, nonintrusive characteristic, and no mass introduction. It is a new type of displacement sensor which is convenient and reliable. This study introduces the singular value decomposition (SVD) methods for video image processing and presents a vibration-extracted algorithm. The algorithms can successfully realize noncontact displacement measurements without undesirable influence to the structure behavior. SVD-based algorithm decomposes a matrix combined with the former frames to obtain a set of orthonormal image bases while the projections of all video frames on the basis describe the vibration information. By means of simulation, the parameters selection of SVD-based algorithm is discussed in detail. To validate the algorithm performance in practice, sinusoidal motion tests are performed. Results indicate that the proposed technique can provide fairly accurate displacement measurement. Moreover, a sound barrier experiment showing how the high-speed rail trains affect the sound barrier nearby is carried out. It is for the first time to be realized at home and abroad due to the challenge of measuring environment.