Skip to main content
SpringerLink
Log in

Mast Arm Monitoring via Traffic Camera Footage: A Pixel-Based Modal Analysis Approach

  • S.I.: Computer Vision and Scanning Laser Vibrometry Methods
  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

Traffic signal mast arm structures must be regularly inspected for cracking, bolt loosening, and other signs of deterioration. Due to large inventories, physical inspections and/or dedicated monitoring systems can be prohibitively time-consuming and expensive to implement at a large scale. However, the growing use of vision-based methods for structural monitoring applications introduces the possibility of leveraging video footage from existing traffic cameras for this purpose. The extraction of dynamic properties (i.e., natural frequencies and damping) from this footage could be employed in detecting possible signs of deterioration. This study presents a vision-based monitoring method which uses a single traffic camera to identify the modal properties of the supporting traffic signal mast arm. This was achieved via operational modal analysis on pixel displacements obtained from a traffic camera mounted on a traffic signal mast arm in Norfolk, VA, monitored during July, 2019. First, sub-pixel displacements were extracted frame-by-frame using weighted centroid tracking of pavement markings. Then, covariance-driven stochastic subspace identification (SSI-Cov) was employed to extract the mast arm fundamental frequencies, damping ratios, and mode shapes. For validation of the vision-based results, SSI-Cov was also applied to acceleration data recorded by two high-sensitivity accelerometers mounted on the structure. In total, the processing was carried out on four different videos and ten acceleration datasets. The vision-based method was able to reliably identify the fundamental frequencies of the structure (Δf < 0.005 Hz mean difference). The associated damping ratios were consistently overestimated but still close in structural terms (Δζ < 0.7% mean difference).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. Obtained from the following manufacturers: https://www.alliedvision.com/en/products/cameras.html and https://www.pelco.com/video-surveillance-camera-security-systems/panoramic-ip.

References

  1. Dexter RJ, Ricker MJ (2002) Fatigue-resistant design of cantilevered signal, sign, and light supports. Technical Report 469, National Cooperative Highway Research Program (NCHRP)

  2. Chen G, Barker M, Dharani LR, Ramsay C (2003) Signal mast arm failure investigation. Technical Report RDT 03-010, Missouri Department of Transportation (MoDOT) Research, Development and Technology

  3. Ocel JM, Dexter RJ, Hajjar JF (2006) Fatigue-resistant design for over head signs, mast-arm signal poles, and lighting standards. Technical Report MN/RC-2006-07, Minnesota Department of Transportation

  4. Letchford C, Cruzado H (2008) Risk assessment model for wind-induced fatigue failure of cantilever traffic signal structures Technical Report FHWA/TX-07-4586-4 Texas Department of Transportation

  5. Alexander LA, Wood J (2009) A study of the low-cycle fatigue failure of a galvanised steel lighting column. Eng Fail Anal 16:2153–2162

    Article  CAS  Google Scholar 

  6. Repetto MP, Solari G (2010) Wind-induced fatigue collapse of real slender structures. Eng Struct 32:3888–3898

    Article  Google Scholar 

  7. Garlich MJ, Thorkildsen ET (2005) Guidelines for the installation, inspection, maintenance and repair of structural supports for highway signs, luminaires, and traffic signals. FHWA NHI 05-036, Federal Highway Administration

  8. Christenson R (2011) Reducing fatigue in wind-excited traffic signal support structures using smart damping technologies. Technical Report NCHRP-IDEA Project 141, Transportation Research Board

  9. McCormick N, Klimaytys G, Lord J (2011) A review of techniques suitable for measuring position or strain in Civil Engineering structures. Technical Report MAT 49, National Physics Laboratory

  10. Feng D, Feng MQ (2018) Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – a review. Eng Struct 156:105–117. https://doi.org/10.1016/j.engstruct.2017.11.018

    Article  Google Scholar 

  11. Harvey Jr PS, Elisha G (2018) Vision-based vibration monitoring using existing cameras installed within a building. Structural Control and Health Monitoring 25:e2235. https://doi.org/10.1002/stc.2235

    Article  Google Scholar 

  12. Hosseinzadeh AZ, Harvey Jr PS (2019) Pixel-based operating modes from surveillance videos for structural vibration monitoring: A preliminary experimental study. Measurement 148:106911. https://doi.org/10.1016/j.measurement.2019.106911

    Article  Google Scholar 

  13. Kastrinaki V, Zervakis M, Kalaitzakis K (2003) A survey of video processing techniques for traffic applications. Image Vis Comput 21:359–381

    Article  Google Scholar 

  14. Fraser M, Elgamal A, He X, Conte JP (2010) Sensor network for structural health monitoring of a highway bridge. J Comput Civ Eng 24(1):11–24

    Article  Google Scholar 

  15. Zaurin R, Catbas FN (2010) Integration of computer imaging and sensor data for structural health monitoring of bridges. Smart Materials and Structures 19:015019. https://doi.org/10.1088/0964-1726/19/1/015019

    Article  Google Scholar 

  16. Gul M, Catbas FN, Hattori H (2015) Image-based monitoring of open gears of movable bridges for condition assessment and maintenance decision making. Journal of Computing in Civil Engineering 29(2):04014034. https://doi.org/10.1061/%28ASCE%29CP.1943-5487.0000307

    Article  Google Scholar 

  17. Fu G, Moosa AG (2002) An optical approach to structural displacement measurement and its application. Journal of Engineering Mechanics 128:511–520. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:5(511)

    Article  Google Scholar 

  18. Wahbeh AM, Caffrey JP, Masri SF (2003) A vision-based approach for the direct measurement of displacements in vibrating systems. Smart Materials and Structures 12:785–794. https://doi.org/10.1088/0964-1726/12/5/016

    Article  Google Scholar 

  19. Lee JJ, Shinozuka M (2006) Real-time displacement measurement of a flexible bridge using digital image processing techniques. Experimental Mechanics 46:105–115. https://doi.org/10.1007/s11340-006-6124-2

    Article  Google Scholar 

  20. Harvey Jr. PS, Zéhil G-P, Gavin HP (2014) Experimental validation of simplified models for rolling isolation systems. Earthquake Engineering and Structural Dynamics 43:1067–1088. https://doi.org/10.1002/eqe.2387

    Article  Google Scholar 

  21. Tateishi K, Hanji T (2005) Application of image analysis to steel structural engineering. In: Ansari F (ed) Sensing issues in civil structural health monitoring. Springer, pp 249–258

  22. Jahanshani MR, Masri SF (2011) Robust vision-based approaches for structural health monitoring. In: Proceedings of the 8th International workshop on structural health monitoring. Stanford University, Stanford, CA, pp 2252–2259

  23. Shariati A, Schumacher T, Ramanna N (2014) Exploration of video-based structural health monitoring techniques. Technical Report CAIT-UTC-038 Center for Advanced Infrastructure and Transportation

  24. Cheng C, Kawaguchi K (2015) A preliminary study on the response of steel structures using surveillance camera image with vision-based method during the Great East Japan Earthquake. Measurement 62:142–148. https://doi.org/10.1016/j.measurement.2014.10.039

    Article  Google Scholar 

  25. Irani M, Rousso B, Peleg S (1994) Recovery of ego-motion using image stabilization. In: 1994 IEEE computer society conference on computer vision and pattern recognition, pp 454–460

  26. Fermüller C, Aloimonos Y (1995) Direct perception of three-dimensional motion from patterns of visual moion. Science 270:1973–1976

    Article  Google Scholar 

  27. Stein GP, Mano O, Shashua A (2000) A robust method for computing vehicle ego-motion. In: Proceedings of the IEEE intelligent vehicles symposium, pp 362–368

  28. Spencer BF (2018) Structural displacement measurement using an unmanned aerial system. Computer-Aided Civil and Infrastructure Engineering 33(3):183–192. ISSN 14678667. https://doi.org/10.1111/mice.12338

    Article  Google Scholar 

  29. Lee J, Lee KC, Jeong S, Lee YJ, Sim SH (2020) Long-term displacement measurement of full-scale bridges using camera ego-motion compensation. Mechanical Systems and Signal Processing 140:106651. ISSN 10961216. https://doi.org/10.1016/j.ymssp.2020.106651

    Article  Google Scholar 

  30. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334. ISSN 01628828. https://doi.org/10.1109/34.888718

    Article  Google Scholar 

  31. Dilena M, Morassi A (2009) Structural health monitoring of rods based on natural frequency and antiresonant frequency measurements. Struct Heal Monit 8(2):149–173. ISSN 14759217. https://doi.org/10.1177/1475921708102103

    Article  Google Scholar 

  32. Ren WX, De Roeck G (2002) Structural damage identification using modal data. II: Test verification. J Struct Eng 128(1):96–104. ISSN 07339445. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:1(96)

    Article  Google Scholar 

  33. Peeters B, De Roeck G (1999) Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing 13(6):855–878. ISSN 08883270. https://doi.org/10.1006/mssp.1999.1249

    Article  Google Scholar 

  34. Van Overschee P, Moor BD (1996) Subspace identification for linear system: Theory - Implementation - Applications, 1st edn. Kluwer Academic Publishers, Boston. ISBN 0792397177. https://doi.org/10.1109/IEMBS.2008.4650193

    Book  Google Scholar 

  35. Sarlo R, Tarazaga PA, Kasarda ME (2018) High resolution operational modal analysis on a five-story smart building under wind and human induced excitation. Eng Struct 176(August):279–292. ISSN 18737323. https://doi.org/10.1016/j.engstruct.2018.08.060

    Article  Google Scholar 

  36. Döhler M, Mevel L (2013) Efficient multi-order uncertainty computation for stochastic subspace identification. Mechanical Systems and Signal Processing 38(2):346–366. ISSN 08883270. https://doi.org/10.1016/j.ymssp.2013.01.012

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Virginia Department of Transportation (VDOT) personnel, Richard Crissman and Stephen Stanley, who graciously facilitated the deployment and removal operations. In addition, the authors acknowledge the Norfolk Police Department for their assistance in the lane closure operations.

Funding

The instrumentation and monitoring phases of this project were funded by the Virginia Transportation Research Council and the Virginia Department of Transportation, grant VTRC-MOA #19-122.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA

    M. H. SoleimaniBabakamali, A. Moghadam, R. Sarlo & M. H. Hebdon

  2. School of Civil Engineering, Environmental Science, University of Oklahoma, Norman, OK, USA

    P. S. Harvey Jr.

Authors
  1. M. H. SoleimaniBabakamali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Moghadam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Sarlo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. H. Hebdon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. S. Harvey Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Sarlo.

Ethics declarations

Conflict of interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Availability of data and materials

Data and materials will be provided upon request.

Code availability

Operational modal analysis MATLAB code is publicly available at: https://code.vt.edu/vibes-lab/modal-analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

SoleimaniBabakamali, M.H., Moghadam, A., Sarlo, R. et al. Mast Arm Monitoring via Traffic Camera Footage: A Pixel-Based Modal Analysis Approach. Exp Tech 45, 329–343 (2021). https://doi.org/10.1007/s40799-020-00422-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40799-020-00422-4

Keywords

Search

Navigation