{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:05:27Z","timestamp":1783400727771,"version":"3.54.6"},"reference-count":61,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T00:00:00Z","timestamp":1662854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","award":["DE-AR0001155"],"award-info":[{"award-number":["DE-AR0001155"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Center for Nuclear Energy Facilities and Structures at North Carolina State University","award":["DE-AR0001155"],"award-info":[{"award-number":["DE-AR0001155"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment\u2014a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.<\/jats:p>","DOI":"10.3390\/s22186869","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"6869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5268-6441","authenticated-orcid":false,"given":"Guang-Yu","family":"Nie","sequence":"first","affiliation":[{"name":"Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5849-7468","authenticated-orcid":false,"given":"Saran Srikanth","family":"Bodda","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5595-1351","authenticated-orcid":false,"given":"Harleen Kaur","family":"Sandhu","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2995-8381","authenticated-orcid":false,"given":"Kevin","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9330-7287","authenticated-orcid":false,"given":"Abhinav","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/15732479.2019.1650078","article-title":"Structural displacement monitoring using deep learning-based full field optical flow methods","volume":"16","author":"Dong","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fradelos, Y., Thalla, O., Biliani, I., and Stiros, S. (2020). Study of lateral displacements and the natural frequency of a pedestrian bridge using low-cost cameras. Sensors, 20.","DOI":"10.3390\/s20113217"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kalybek, M., Bocian, M., and Nikitas, N. (2021). Performance of Optical Structural Vibration Monitoring Systems in Experimental Modal Analysis. Sensors, 21.","DOI":"10.3390\/s21041239"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Feng, D., and Feng, M.Q. (2021). Computer Vision for Structural Dynamics and Health Monitoring, John Wiley & Sons.","DOI":"10.1002\/9781119566557"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1017\/S0373463321000540","article-title":"Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework","volume":"74","author":"Chen","year":"2021","journal-title":"J. Navig."},{"key":"ref_6","first-page":"1","article-title":"Effect of Track-Seeking Motion on Off-Track Vibrations of the Head-Gimbal Assembly in HDDs","volume":"54","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Magn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1080\/15732479.2016.1164729","article-title":"Computer vision-based displacement and vibration monitoring without using physical target on structures","volume":"13","author":"Khuc","year":"2017","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111224","DOI":"10.1016\/j.engstruct.2020.111224","article-title":"Investigation of vibration serviceability of a footbridge using computer vision-based methods","volume":"224","author":"Dong","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"04019062","DOI":"10.1061\/(ASCE)ST.1943-541X.0002321","article-title":"Vision-based modal survey of civil infrastructure using unmanned aerial vehicles","volume":"145","author":"Hoskere","year":"2019","journal-title":"J. Struct. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Choi, H., Kang, B., and Kim, D. (2022). Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator. Sensors, 22.","DOI":"10.3390\/s22082878"},{"key":"ref_11","unstructured":"Lucas, B.D., and Kanade, T. (1981, January 24\u201328). An Iterative Image Registration Technique with an Application to Stereo Vision. Proceedings of the DARPA Image Understanding Workshop, Vancouver, BC, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s13349-017-0261-4","article-title":"Review of machine-vision based methodologies for displacement measurement in civil structures","volume":"8","author":"Xu","year":"2018","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2009","DOI":"10.1002\/stc.2009","article-title":"Pixel-wise structural motion tracking from rectified repurposed videos","volume":"24","author":"Khaloo","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kalybek, M., Bocian, M., Pakos, W., Grosel, J., and Nikitas, N. (2021). Performance of Camera-Based Vibration Monitoring Systems in Input-Output Modal Identification Using Shaker Excitation. Remote Sens., 13.","DOI":"10.3390\/rs13173471"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106911","DOI":"10.1016\/j.measurement.2019.106911","article-title":"Pixel-based operating modes from surveillance videos for structural vibration monitoring: A preliminary experimental study","volume":"148","author":"Hosseinzadeh","year":"2019","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.ymssp.2018.11.015","article-title":"Development and field testing of a vision-based displacement system using a low cost wireless action camera","volume":"121","author":"Lydon","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1002\/stc.1850","article-title":"Target-free approach for vision-based structural system identification using consumer-grade cameras","volume":"23","author":"Yoon","year":"2016","journal-title":"Struct. Control Health Monit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1177\/1475921718806895","article-title":"Marker-free monitoring of the grandstand structures and modal identification using computer vision methods","volume":"18","author":"Dong","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1177\/1475921714522841","article-title":"Virtual visual sensors and their application in structural health monitoring","volume":"13","author":"Song","year":"2014","journal-title":"Struct. Health Monit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1177\/1369433219856171","article-title":"A non-target structural displacement measurement method using advanced feature matching strategy","volume":"22","author":"Dong","year":"2019","journal-title":"Adv. Struct. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e1852","DOI":"10.1002\/stc.1852","article-title":"Completely contactless structural health monitoring of real-life structures using cameras and computer vision","volume":"24","author":"Khuc","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ehrhart, M., and Lienhart, W. (2015, January 9\u201312). Development and evaluation of a long range image-based monitoring system for civil engineering structures. Proceedings of the Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, International Society for Optics and Photonics, San Diego, CA, USA.","DOI":"10.1117\/12.2084221"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.compstruc.2018.02.001","article-title":"A computer vision approach for the load time history estimation of lively individuals and crowds","volume":"200","author":"Celik","year":"2018","journal-title":"Comput. Struct."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1006\/cviu.1996.0006","article-title":"The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields","volume":"63","author":"Black","year":"1996","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, D., Roth, S., and Black, M.J. (2010, January 13\u201318). Secrets of optical flow estimation and their principles. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539939"},{"key":"ref_27","unstructured":"Farneback, G. (2001, January 7\u201314). Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vancouver, BC, Canada."},{"key":"ref_28","unstructured":"Farneback, G. (2000, January 3\u20138). Fast and accurate motion estimation using orientation tensors and parametric motion models. Proceedings of the 15th International Conference on Pattern Recognition, ICPR-2000, Barcelona, Spain."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Farneb\u00e4ck, G. (2003). Two-frame motion estimation based on polynomial expansion. Scandinavian Conference on Image Analysis, Springer.","DOI":"10.1007\/3-540-45103-X_50"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Won, J., Park, J.W., Park, K., Yoon, H., and Moon, D.S. (2019). Non-target structural displacement measurement using reference frame-based deepflow. Sensors, 19.","DOI":"10.3390\/s19132992"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/s11263-016-0908-3","article-title":"Deepmatching: Hierarchical deformable dense matching","volume":"120","author":"Revaud","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., and Schmid, C. (2013, January 1\u20138). DeepFlow: Large displacement optical flow with deep matching. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.175"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2017, January 21\u201326). Flownet 2.0: Evolution of optical flow estimation with deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ymssp.2015.06.004","article-title":"Dynamic displacement measurement of large-scale structures based on the Lucas\u2013Kanade template tracking algorithm","volume":"66","author":"Guo","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"05018014","DOI":"10.1061\/(ASCE)BE.1943-5592.0001330","article-title":"Accurate deformation monitoring on bridge structures using a cost-effective sensing system combined with a camera and accelerometers: Case study","volume":"24","author":"Xu","year":"2019","journal-title":"J. Bridge Eng."},{"key":"ref_36","unstructured":"Xu, Y., Brownjohn, J., Hester, D., and Koo, K. (2016, January 5\u20138). Dynamic displacement measurement of a long span bridge using vision-based system. Proceedings of the 8th European Workshop On Structural Health Monitoring (EWSHM 2016), Bilbao, Spain."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0141-0296(93)90054-8","article-title":"Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge","volume":"15","author":"Stephen","year":"1993","journal-title":"Eng. Struct."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"16557","DOI":"10.3390\/s150716557","article-title":"A vision-based sensor for noncontact structural displacement measurement","volume":"15","author":"Feng","year":"2015","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110551","DOI":"10.1016\/j.engstruct.2020.110551","article-title":"Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation","volume":"213","author":"Ngeljaratan","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1002\/we.2505","article-title":"Multicamera measurement system to evaluate the dynamic response of utility-scale wind turbine blades","volume":"23","author":"Poozesh","year":"2020","journal-title":"Wind Energy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"114103","DOI":"10.1117\/1.OE.55.11.114103","article-title":"Vision-based displacement measurement sensor using modified Taylor approximation approach","volume":"55","author":"Liu","year":"2016","journal-title":"Opt. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1687814018780052","DOI":"10.1177\/1687814018780052","article-title":"Accurate vision-based displacement and vibration analysis of bridge structures by means of an image-assisted total station","volume":"10","author":"Omidalizarandi","year":"2018","journal-title":"Adv. Mech. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"109634","DOI":"10.1016\/j.engstruct.2019.109634","article-title":"Damage identification for frame structures using vision-based measurement","volume":"199","author":"Guo","year":"2019","journal-title":"Eng. Struct."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, D., Guo, J., Lei, X., and Zhu, C. (2016). A high-speed vision-based sensor for dynamic vibration analysis using fast motion extraction algorithms. Sensors, 16.","DOI":"10.3390\/s16040572"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"015903","DOI":"10.1088\/1361-6501\/28\/1\/015903","article-title":"Vision-based measurement system for structural vibration monitoring using non-projection quasi-interferogram fringe density enhanced by spectrum correction method","volume":"28","author":"Zhong","year":"2016","journal-title":"Meas. Sci. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"04019010","DOI":"10.1061\/(ASCE)BE.1943-5592.0001363","article-title":"Field deployment and laboratory evaluation of 2D digital image correlation for deflection sensing in complex environments","volume":"24","author":"Alipour","year":"2019","journal-title":"J. Bridge Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Harmanci, Y.E., G\u00fclan, U., Holzner, M., and Chatzi, E. (2019). A novel approach for 3D-structural identification through video recording: Magnified tracking. Sensors, 19.","DOI":"10.3390\/s19051229"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Aoyama, T., Li, L., Jiang, M., Takaki, T., Ishii, I., Yang, H., Umemoto, C., Matsuda, H., Chikaraishi, M., and Fujiwara, A. (2019). Vision-based modal analysis using multiple vibration distribution synthesis to inspect large-scale structures. J. Dyn. Syst. Meas. Control, 141.","DOI":"10.1115\/1.4041604"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nie, G.Y., Cheng, M.M., Liu, Y., Liang, Z., Fan, D.P., Liu, Y., and Wang, Y. (2019, January 15\u201320). Multi-Level Context Ultra-Aggregation for Stereo Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00340"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006). Machine Learning for High-Speed Corner Detection, Springer. European Conference on Computer Vision.","DOI":"10.1007\/11744023_34"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: Binary Robust Independent Elementary Features, Springer. European Conference on Computer Vision.","DOI":"10.1007\/978-3-642-15561-1_56"},{"key":"ref_53","first-page":"33","article-title":"Pyramid methods in image processing","volume":"29","author":"Adelson","year":"1984","journal-title":"RCA Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/2184319.2184337","article-title":"Real-time computer vision with OpenCV","volume":"55","author":"Pulli","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Han, L., Li, Z., Zhong, K., Cheng, X., Luo, H., Liu, G., Shang, J., Wang, C., and Shi, Y. (2019). Vibration detection and motion compensation for multi-frequency phase-shifting-based 3d sensors. Sensors, 19.","DOI":"10.3390\/s19061368"},{"key":"ref_56","unstructured":"Sandhu, H.K. (2021). Artificial Intelligence Based Condition Monitoring of Nuclear Piping-Equipment Systems. [Ph.D. Thesis, North Carolina State University]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s00024-021-02915-8","article-title":"A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference","volume":"179","author":"Bodda","year":"2022","journal-title":"Pure Appl. Geophys."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Jiang, S., Campbell, D., Lu, Y., Li, H., and Hartley, R. (2021). Learning to Estimate Hidden Motions with Global Motion Aggregation. arXiv.","DOI":"10.1109\/ICCV48922.2021.00963"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1016\/j.patcog.2014.01.005","article-title":"Automatic generation and detection of highly reliable fiducial markers under occlusion","volume":"47","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"164899","DOI":"10.1016\/j.nima.2020.164899","article-title":"Tracking algorithms for TPCs using consensus-based robust estimators","volume":"988","author":"Zamora","year":"2021","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip."},{"key":"ref_61","first-page":"8509164","article-title":"A Review of Keypoints\u2019 Detection and Feature Description in Image Registration","volume":"2021","author":"Liu","year":"2021","journal-title":"Sci. Program."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6869\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:29:25Z","timestamp":1760142565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,11]]},"references-count":61,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186869"],"URL":"https:\/\/doi.org\/10.3390\/s22186869","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,11]]}}}