Recent advances in computer vision techniques allow to obtain information on the dynamic behavior of structures using commercial grade video recording devices. The advantage of such schemes is due to the non-invasive nature of video recording, and the ability to extract information at a high spatial density utilizing features on the structure. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications.
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Computer Vision Aided Structural Identification: Feature Tracking Using Particle Tracking Velocimetry versus Optical Flow
Published: 14 November 2018 by MDPI in 5th International Electronic Conference on Sensors and Applications session Structural Health Monitoring Technologies and Sensor Networks
Keywords: Computer Vision; Particle Tracking Velocimetry; Optical Flow; System Identification; Modal Analysis; Phase-based Motion Magnification