Target-less computer vision for traffic signal structure vibration studies

Bartilson, Daniel Thomas; Wieghaus, Kyle T.; Hurlebaus, Stefan

doi:10.1016/j.ymssp.2015.01.005

Cited by 49 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By applying subpixel methods, vision sensors are capable of simultaneous multi‐target displacement monitoring with only one video camera (Luo et al., ). Various vision‐sensing techniques have been developed and applied for displacement measurement such as digital image correlation (DIC) (Mazzoleni and Zappa, ; Cigada et al., ; Dworakowski et al., ), phase‐based method (Chen et al., ), orientation‐code‐matching (OCM) technique, optical flow, binary thresholding (Lee and Shinozuka, ), and others (Bartilson et al., ; Oh et al., ; Santos et al., ).…”

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

View full text Add to dashboard Cite

Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…However, this nullifies the easy-to-instrument advantage of the camera as the targets need to be placed on the structure (Wahbeh et al 2003; Lee and Shinozuka 2006;Kim and Kim 2011;Cigada et al 2014). Target-less measurement, a much more ideal use which does not require any preparation other than the camera itself where virtual sensors are imposed over the video (Schumacher and Shariati 2013), have also been made of bridges and cables (Kim and Kim 2013;Cigada et al 2014) and a traffic structure (Bartilson et al 2015). Particularly impressive is the Caetano et al (2011) study where frequencies of cables in a cable-stayed bridge were identified from a distance of 850 m with the smallest estimated amplitude being approximately 1=3 of a pixel.…”

Section: Introductionmentioning

confidence: 99%

Video Camera–Based Vibration Measurement for Civil Infrastructure Applications

Chen

Davis

Wadhwa

et al. 2017

J. Infrastruct. Syst.

146

View full text Add to dashboard Cite

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.

show abstract

“…There are numerous algorithms proper for calculating particle's velocity. In this study, minimum quadratic difference (MQD) method (Gui and Merzkirch, ; Bartilson et al., ) has been employed because of its high precision and robustness. The details of MQD algorithm are shown in Figure .…”

Section: Measuring the Human‐induced Impact Forces And Structural Sysmentioning

confidence: 99%

Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry

Tian

Zhang

2018

Computer aided Civil Eng

View full text Add to dashboard Cite

The rapid impact testing of bridges contains unique advantages. For example, structural parameters, including frequency response function and structural flexibility matrix can be identified; however, additional impact-testing instruments are required to excite a bridge, restricting the efficiency of the measurement strategy in terms of experimental cost and time. In this paper, a particle image velocimetry-based method is proposed for the rapid impact testing and system identification of footbridges under pedestrian excitations. The proposed method has shown promising features: (1) pedestrian load is utilized for the impact excitation of footbridges, which is more convenient than the conventional impacttesting method with additional excitation devices; (2) the human-induced impact forces under varying jumping scenarios are calculated from image sequences of human motions acquired by a single camera with its noncontact and target-less characteristics; and (3) both humaninduced impact forces (inputs) and structural responses (outputs) are employed to identify more modal parameters (i.e., scaling factors, modal mass, and structural flexibility). The robustness of the proposed method was successfully validated by a laboratory test of a simply supported beam and field testing of a cable-stayed footbridge. The proposed method not only could improve

show abstract

Target-less computer vision for traffic signal structure vibration studies

Cited by 49 publications

References 20 publications

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Video Camera–Based Vibration Measurement for Civil Infrastructure Applications

Rapid Impact Testing and System Identification of Footbridges Using Particle Image Velocimetry

Contact Info

Product

Resources

About