Skip to main content
SpringerLink
Log in

Computer Vision Techniques in Construction: A Critical Review

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Computer vision has been gaining interest in a wide range of research areas in recent years, from medical to industrial robotics. The architecture, engineering and construction and facility management sector ranks as one of the most intensive fields where vision-based systems/methods are used to facilitate decision making processes during the construction phase. Construction sites make efficient monitoring extremely tedious and difficult due to clutter and disorder. Extensive research has been carried out to investigate the potential to utilise computer vision for assisting on-site managerial tasks. This paper reviews studies on computer vision in the past decade, with a focus on state-of-the-art methods in a typical vision-based scheme, and discusses challenges associated with their application. This research aims to guide practitioners to successfully find suitable approaches for a particular project.

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

Similar content being viewed by others

References

  1. Lowe D (2015) The Computer Vision Industry. https://www.cs.ubc.ca/~lowe/vision.html. Accessed 11 Nov 2019

  2. Forsyth DA, Ponce J (2002) Computer vision: a modern approach. Prentice Hall Professional Technical Reference, Upper Saddle River

    Google Scholar 

  3. Yang J et al (2015) Construction performance monitoring via still images, time-lapse photos, and video streams: now, tomorrow, and the future. Adv Eng Inform 29(2):211–224

    Article  Google Scholar 

  4. Han KK, Golparvar-Fard M (2017) Potential of big visual data and building information modeling for construction performance analytics: an exploratory study. Autom Constr 73:184–198

    Article  Google Scholar 

  5. Wang D, Dai F, Ning X (2015) Risk assessment of work-related musculoskeletal disorders in construction: state-of-the-art review. J Constr Eng Manag 141(6):04015008

    Article  Google Scholar 

  6. Seo J et al (2015) Computer vision techniques for construction safety and health monitoring. Adv Eng Inform 29(2):239–251

    Article  Google Scholar 

  7. Teizer J (2015) Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Adv Eng Inform 29(2):225–238

    Article  MathSciNet  Google Scholar 

  8. Ranaweera K, Ruwanpura J, Fernando S (2013) Automated real-time monitoring system to measure shift production of tunnel construction projects. J Comput Civ Eng 27(1):68–77

    Article  Google Scholar 

  9. Rebolj D et al (2017) Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. Autom Constr 84:323–334

    Article  Google Scholar 

  10. Fang Q et al (2018) Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Autom Constr 85:1–9

    Article  Google Scholar 

  11. Fang W et al (2018) Automated detection of workers and heavy equipment on construction sites: a convolutional neural network approach. Adv Eng Inform 37:139–149

    Article  Google Scholar 

  12. Luo X, Li H, Yang X, Yu Y, Cao D (2019) Capturing and understanding workers’ activities in far-field surveillance videos with deep action recognition and Bayesian nonparametric learning. Comput Aided Civ Infrastruct Eng 34(4):333–351

    Article  Google Scholar 

  13. Azar ER, Dickinson S, McCabe B (2013) Server-customer interaction tracker: computer vision-based system to estimate dirt-loading cycles. J Constr Eng Manag 139(7):785–794

    Article  Google Scholar 

  14. Luo X et al (2018) Towards efficient and objective work sampling: recognizing workers’ activities in site surveillance videos with two-stream convolutional networks. Autom Constr 94:360–370

    Article  Google Scholar 

  15. Mneymneh BE, Abbas M, Khoury H (2019) Vision-based framework for intelligent monitoring of hardhat wearing on construction sites. J Comput Civ Eng 33(2):04018066

    Article  Google Scholar 

  16. Wu Y et al (2010) Object recognition in construction-site images using 3D CAD-based filtering. J Comput Civ Eng 24(1):56–64

    Article  Google Scholar 

  17. Kim J, Chi S, Seo J (2018) Interaction analysis for vision-based activity identification of earthmoving excavators and dump trucks. Autom Constr 87:297–308

    Article  Google Scholar 

  18. Hui L, Park M-W, Brilakis I (2015) Automated brick counting for façade construction progress estimation. J Comput Civ Eng 29(6):04014091

    Article  Google Scholar 

  19. Bae H, Golparvar-Fard M, White J (2015) Image-based localization and content authoring in structure-from-motion point cloud models for real-time field reporting applications. J Comput Civ Eng 29(4):B4014008

    Article  Google Scholar 

  20. Park M-W, Elsafty N, Zhu Z (2015) Hardhat-wearing detection for enhancing on-site safety of construction workers. J Constr Eng Manag 141(9):04015024

    Article  Google Scholar 

  21. Zhu Z, Ren X, Chen Z (2016) Visual tracking of construction jobsite workforce and equipment with particle filtering. J Comput Civ Eng 30(6):04016023

    Article  Google Scholar 

  22. Gong J, Caldas CH, Gordon C (2011) Learning and classifying actions of construction workers and equipment using Bag-of-Video-Feature-Words and Bayesian network models. Adv Eng Inform 25(4):771–782

    Article  Google Scholar 

  23. Yang J, Shi Z, Wu Z (2016) Vision-based action recognition of construction workers using dense trajectories. Adv Eng Inform 30(3):327–336

    Article  Google Scholar 

  24. Yuan C, Li S, Cai H (2017) Vision-based excavator detection and tracking using hybrid kinematic shapes and key nodes. J Comput Civ Eng 31(1):04016038

    Article  Google Scholar 

  25. Konstantinou E, Brilakis I (2018) Matching construction workers across views for automated 3D vision tracking on-site. J Constr Eng Manag 144(7):04018061

    Article  Google Scholar 

  26. Soltani MM, Zhu Z, Hammad A (2018) Framework for location data fusion and pose estimation of excavators using stereo vision. J Comput Civ Eng 32(6):04018045

    Article  Google Scholar 

  27. Dai F, Lu M (2013) Three-dimensional modeling of site elements by analytically processing image data contained in site photos. J Constr Eng Manag 139(7):881–894

    Article  Google Scholar 

  28. Golparvar-Fard M, Peña-Mora F, Savarese S (2011) Integrated sequential as-built and as-planned representation with D4AR tools in support of decision-making tasks in the AEC/FM industry. J Constr Eng Manag 137(12):1099–1116

    Article  Google Scholar 

  29. Park M-W, Koch C, Brilakis I (2012) Three-dimensional tracking of construction resources using an on-site camera system. J Comput Civ Eng 26(4):541–549

    Article  Google Scholar 

  30. Kim H, Kim H (2018) 3D reconstruction of a concrete mixer truck for training object detectors. Autom Constr 88:23–30

    Article  Google Scholar 

  31. Chi S, Caldas CH (2012) Image-based safety assessment: automated spatial safety risk identification of earthmoving and surface mining activities. J Constr Eng Manag 138(3):341–351

    Article  Google Scholar 

  32. Son H, Kim C (2010) 3D structural component recognition and modeling method using color and 3D data for construction progress monitoring. Autom Constr 19(7):844–854

    Article  Google Scholar 

  33. Han S, Lee S (2013) A vision-based motion capture and recognition framework for behavior-based safety management. Autom Constr 35:131–141

    Article  Google Scholar 

  34. Seo J et al (2015) Motion data-driven biomechanical analysis during construction tasks on sites. J Comput Civ Eng 29(4):B4014005

    Article  Google Scholar 

  35. Guo H et al (2018) Image-and-skeleton-based parameterized approach to real-time identification of construction workers’ unsafe behaviors. J Constr Eng Manag 144(6):04018042

    Article  Google Scholar 

  36. Han S, Lee S, Peña-Mora F (2014) Comparative study of motion features for similarity-based modeling and classification of unsafe actions in construction. J Comput Civ Eng 28(5):A4014005

    Article  Google Scholar 

  37. Han S, Lee S, Peña-Mora F (2013) Vision-based detection of unsafe actions of a construction worker: case study of ladder climbing. J Comput Civ Eng 27(6):635–644

    Article  Google Scholar 

  38. Turkan Y et al (2013) Toward automated earned value tracking using 3D imaging tools. J Constr Eng Manag 139(4):423–433

    Article  Google Scholar 

  39. Rausch C et al (2017) Optimum assembly planning for modular construction components. J Comput Civ Eng 31(1):04016039

    Article  Google Scholar 

  40. Chen J et al (2017) Principal axes descriptor for automated construction-equipment classification from point clouds. J Comput Civ Eng 31(2):04016058

    Article  Google Scholar 

  41. Sharif M-M et al (2017) Automated model-based finding of 3D objects in cluttered construction point cloud models. Comput Aided Civ Infrastruct Eng 32(11):893–908

    Article  Google Scholar 

  42. Wang Q, Cheng JCP, Sohn H (2017) Automated estimation of reinforced precast concrete rebar positions using colored laser scan data. Comput Aided Civ Infrastruct Eng 32(9):787–802

    Article  Google Scholar 

  43. Rausch C et al (2017) Kinematics chain based dimensional variation analysis of construction assemblies using building information models and 3D point clouds. Autom Constr 75:33–44

    Article  Google Scholar 

  44. Teizer J, Allread BS, Mantripragada U (2010) Automating the blind spot measurement of construction equipment. Autom Constr 19(4):491–501

    Article  Google Scholar 

  45. Roh S, Aziz Z, Peña-Mora F (2011) An object-based 3D walk-through model for interior construction progress monitoring. Autom Constr 20(1):66–75

    Article  Google Scholar 

  46. Jeelani I, Han K, Albert A (2018) Automating and scaling personalized safety training using eye-tracking data. Autom Constr 93:63–77

    Article  Google Scholar 

  47. Wang Z, Li H, Zhang X (2019) Construction waste recycling robot for nails and screws: computer vision technology and neural network approach. Autom Constr 97:220–228

    Article  Google Scholar 

  48. Azar ER (2017) Semantic annotation of videos from equipment-intensive construction operations by shot recognition and probabilistic reasoning. J Comput Civ Eng 31(5):04017042

    Article  Google Scholar 

  49. Golparvar-Fard M, Heydarian A, Niebles JC (2013) Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers. Adv Eng Inform 27(4):652–663

    Article  Google Scholar 

  50. Khosrowpour A, Niebles JC, Golparvar-Fard M (2014) Vision-based workface assessment using depth images for activity analysis of interior construction operations. Autom Constr 48:74–87

    Article  Google Scholar 

  51. Yang J et al (2014) Vision-based tower crane tracking for understanding construction activity. J Comput Civ Eng 28(1):103–112

    Article  Google Scholar 

  52. Irizarry J, Costa DB (2016) Exploratory study of potential applications of unmanned aerial systems for construction management tasks. J Manag Eng 32(3):05016001

    Article  Google Scholar 

  53. Bang S, Kim H, Kim H (2017) UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Autom Constr 84:70–80

    Article  Google Scholar 

  54. Golparvar-Fard M, Peña-Mora F, Savarese S (2009) D4AR–a 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication. J Inf Technol Constr 14(13):129–153

    Google Scholar 

  55. Ergan S et al (2008) Technological assessment and process implications of field data capture technologies for construction and facility/infrastructure management. Electron J Inf Technol Constr 13:134–154

    Google Scholar 

  56. Cho YK, Gai M (2014) Projection-recognition-projection method for automatic object recognition and registration for dynamic heavy equipment operations. J Comput Civ Eng 28(5):A4014002

    Article  Google Scholar 

  57. Kim H, Kim K, Kim H (2016) Data-driven scene parsing method for recognizing construction site objects in the whole image. Autom Constr 71:271–282

    Article  Google Scholar 

  58. Chen J, Fang Y, Cho YK (2017) Real-time 3D crane workspace update using a hybrid visualization approach. J Comput Civ Eng 31(5):04017049

    Article  Google Scholar 

  59. Azar ER, McCabe B (2012) Automated visual recognition of dump trucks in construction videos. J Comput Civ Eng 26(6):769–781

    Article  Google Scholar 

  60. Ding L et al (2018) A deep hybrid learning model to detect unsafe behavior: integrating convolution neural networks and long short-term memory. Autom Constr 86:118–124

    Article  Google Scholar 

  61. Kim C, Son H, Kim C (2013) Automated construction progress measurement using a 4D building information model and 3D data. Autom Constr 31:75–82

    Article  Google Scholar 

  62. Park M-W, Brilakis I (2012) Construction worker detection in video frames for initializing vision trackers. Autom Constr 28:15–25

    Article  Google Scholar 

  63. Park M-W, Brilakis I (2016) Continuous localization of construction workers via integration of detection and tracking. Autom Constr 72:129–142

    Article  Google Scholar 

  64. Hamledari H, McCabe B, Davari S (2017) Automated computer vision-based detection of components of under-construction indoor partitions. Autom Constr 74:78–94

    Article  Google Scholar 

  65. Canny J (1987) A computational approach to edge detection. In: Fischler MA, Firschein O (eds) Readings in computer vision. Morgan Kaufmann, San Francisco, pp 184–203

    Google Scholar 

  66. Sobel I, Feldman G (1968) A 3 × 3 isotropic gradient operator for image processing. Pattern Classif Scene Anal 271–272

  67. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Machine Intell 24(5):603–619

    Article  Google Scholar 

  68. Harris CG, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference. Citeseer

  69. Smith SM, Brady JM (1997) SUSAN—a new approach to low level image processing. Int J Comput Vis 23(1):45–78

    Article  Google Scholar 

  70. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: European conference on computer vision. Springer

  71. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision

  72. Wu H, Zhao J (2018) An intelligent vision-based approach for helmet identification for work safety. Comput Ind 100:267–277

    Article  Google Scholar 

  73. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149). IEEE

  74. Chen J, Fang Y, Cho YK (2018) Performance evaluation of 3D descriptors for object recognition in construction applications. Autom Constr 86:44–52

    Article  Google Scholar 

  75. Zhu Z, Davari K (2015) Comparison of local visual feature detectors and descriptors for the registration of 3D building scenes. J Comput Civ Eng 29(5):04014071

    Article  Google Scholar 

  76. Kolar Z, Chen H, Luo X (2018) Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Autom Constr 89:58–70

    Article  Google Scholar 

  77. Han K, Degol J, Golparvar-Fard M (2018) Geometry- and appearance-based reasoning of construction progress monitoring. J Constr Eng Manag 144(2):04017110

    Article  Google Scholar 

  78. Chen H et al (2019) A proactive workers’ safety risk evaluation framework based on position and posture data fusion. Autom Constr 98:275–288

    Article  Google Scholar 

  79. Fang W et al (2018) Falls from heights: a computer vision-based approach for safety harness detection. Autom Constr 91:53–61

    Article  Google Scholar 

  80. Kim H et al (2018) Detecting construction equipment using a region-based fully convolutional network and transfer learning. J Comput Civ Eng 32(2):04017082

    Article  Google Scholar 

  81. Fang Q et al (2018) Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment. Autom Constr 93:148–164

    Article  Google Scholar 

  82. Kim D et al (2019) Remote proximity monitoring between mobile construction resources using camera-mounted UAVs. Autom Constr 99:168–182

    Article  Google Scholar 

  83. Soltani MM, Zhu Z, Hammad A (2017) Skeleton estimation of excavator by detecting its parts. Autom Constr 82:1–15

    Article  Google Scholar 

  84. Chi S, Caldas CH (2011) Automated object identification using optical video cameras on construction sites. Comput Aided Civ Infrastruct Eng 26(5):368–380

    Article  Google Scholar 

  85. Bai Y, Huan J, Kim S (2012) Measuring bridge construction efficiency using the wireless real-time video monitoring system. J Manag Eng 28(2):120–126

    Article  Google Scholar 

  86. Memarzadeh M, Golparvar-Fard M, Niebles JC (2013) Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Autom Constr 32:24–37

    Article  Google Scholar 

  87. Kim K, Kim H, Kim H (2017) Image-based construction hazard avoidance system using augmented reality in wearable device. Autom Constr 83:390–403

    Article  Google Scholar 

  88. Gouveia LTd et al (2011) Entropy-based approach to analyze and classify mineral aggregates. J Comput Civ Eng 25(1):75–84

    Article  Google Scholar 

  89. Son H, Kim C, Kim C (2012) Automated color model–based concrete detection in construction-site images by using machine learning algorithms. J Comput Civ Eng 26(3):421–433

    Article  Google Scholar 

  90. Kim J, Chi S (2017) Adaptive detector and tracker on construction sites using functional integration and online learning. J Comput Civ Eng 31(5):04017026

    Article  Google Scholar 

  91. Rezazadeh Azar E, McCabe B (2012) Part based model and spatial–temporal reasoning to recognize hydraulic excavators in construction images and videos. Autom Construct 24:194–202

    Article  Google Scholar 

  92. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 5:564–575

    Article  Google Scholar 

  93. Park M-W, Makhmalbaf A, Brilakis I (2011) Comparative study of vision tracking methods for tracking of construction site resources. Autom Constr 20(7):905–915

    Article  Google Scholar 

  94. Zhu Z et al (2016) Predicting movements of onsite workers and mobile equipment for enhancing construction site safety. Autom Constr 68:95–101

    Article  Google Scholar 

  95. Bügler M et al (2017) Fusion of photogrammetry and video analysis for productivity assessment of earthwork processes. Comput Aided Civ Infrastruct Eng 32(2):107–123

    Article  Google Scholar 

  96. Kim H, Kim K, Kim H (2016) Vision-based object-centric safety assessment using fuzzy inference: monitoring struck-by accidents with moving objects. J Comput Civ Eng 30(4):04015075

    Article  Google Scholar 

  97. Lee Y-J, Park M-W (2019) 3D tracking of multiple onsite workers based on stereo vision. Autom Constr 98:146–159

    Article  Google Scholar 

  98. Xiao B, Zhu Z (2018) Two-dimensional visual tracking in construction scenarios: a comparative study. J Comput Civ Eng 32(3):04018006

    Article  Google Scholar 

  99. Camera Calibrator App, in Computer Vision Toolbox™. 2013, MathWorks®. p. The Camera Calibrator app allows you to estimate camera intrinsics, extrinsics, and lens distortion parameters. You can use these camera parameters for various computer vision applications. These applications include removing the effects of lens distortion from an image, measuring planar objects, or reconstructing 3-D scenes from multiple cameras

  100. Bouguet J-Y (2004) Camera Calibration Toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/. Accessed 15 Nov 2019

  101. Khoury H et al (2015) Infrastructureless approach for ubiquitous user location tracking in construction environments. Autom Constr 56:47–66

    Article  Google Scholar 

  102. Rodriguez-Gonzalvez P et al (2014) Image-based modeling of built environment from an unmanned aerial system. Autom Constr 48:44–52

    Article  Google Scholar 

  103. Son H, Kim C, Cho YK (2017) Automated schedule updates using as-built data and a 4D building information model. J Manag Eng 33(4):04017012

    Article  Google Scholar 

  104. Pučko Z, Šuman N, Rebolj D (2018) Automated continuous construction progress monitoring using multiple workplace real time 3D scans. Adv Eng Inform 38:27–40

    Article  Google Scholar 

  105. Turkan Y et al (2012) Automated progress tracking using 4D schedule and 3D sensing technologies. Autom Constr 22:414–421

    Article  Google Scholar 

  106. Golparvar-Fard M et al (2009) Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs. J Comput Civ Eng 23(6):391–404

    Article  Google Scholar 

  107. Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv CSUR 43(3):16

    Google Scholar 

  108. Luo X et al (2018) Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks. J Comput Civ Eng 32(3):04018012

    Article  Google Scholar 

  109. Luo H et al (2018) Convolutional neural networks: computer vision-based workforce activity assessment in construction. Autom Constr 94:282–289

    Article  Google Scholar 

  110. Kong L et al (2018) Quantifying the physical intensity of construction workers, a mechanical energy approach. Adv Eng Inform 38:404–419

    Article  Google Scholar 

  111. Zhang H, Yan X, Li H (2018) Ergonomic posture recognition using 3D view-invariant features from single ordinary camera. Autom Constr 94:1–10

    Article  Google Scholar 

  112. Ray SJ, Teizer J (2013) Computing 3D blind spots of construction equipment: implementation and evaluation of an automated measurement and visualization method utilizing range point cloud data. Autom Constr 36:95–107

    Article  Google Scholar 

  113. Ibrahim YM et al (2009) Towards automated progress assessment of workpackage components in construction projects using computer vision. Adv Eng Inform 23(1):93–103

    Article  MathSciNet  Google Scholar 

  114. Zhang X et al (2009) Automating progress measurement of construction projects. Autom Constr 18(3):294–301

    Article  Google Scholar 

  115. Han KK, Golparvar-Fard M (2015) Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs. Autom Constr 53:44–57

    Article  Google Scholar 

  116. Dimitrov A, Golparvar-Fard M (2014) Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections. Adv Eng Inform 28(1):37–49

    Article  Google Scholar 

  117. Kim M-K et al (2015) A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning. Autom Constr 49:225–238

    Article  Google Scholar 

  118. Tang P, Huber D, Akinci B (2011) Characterization of laser scanners and algorithms for detecting flatness defects on concrete surfaces. J Comput Civ Eng 25(1):31–42

    Article  Google Scholar 

  119. Golparvar-Fard M, Peña-Mora F, Savarese S (2015) Automated progress monitoring using unordered daily construction photographs and IFC-based building information models. J Comput Civ Eng 29(1):04014025

    Article  Google Scholar 

  120. Wang C, Cho YK (2015) Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud. Autom Constr 49:239–249

    Article  Google Scholar 

  121. Azar ER (2016) Construction equipment identification using marker-based recognition and an active zoom camera. J Comput Civ Eng 30(3):04015033

    Article  Google Scholar 

  122. Asadi K et al (2018) Vision-based integrated mobile robotic system for real-time applications in construction. Autom Constr 96:470–482

    Article  Google Scholar 

  123. Shahandashti SM et al (2011) Data-fusion approaches and applications for construction engineering. J Constr Eng Manag 137(10):863–869

    Article  Google Scholar 

  124. Soltani MM, Zhu Z, Hammad A (2016) Automated annotation for visual recognition of construction resources using synthetic images. Autom Constr 62:14–23

    Article  Google Scholar 

  125. Liu K, Golparvar-Fard M (2015) Crowdsourcing construction activity analysis from jobsite video streams. J Constr Eng Manag 141(11):04015035

    Article  Google Scholar 

  126. Han KK, Cline D, Golparvar-Fard M (2015) Formalized knowledge of construction sequencing for visual monitoring of work-in-progress via incomplete point clouds and low-LoD 4D BIMs. Adv Eng Inform 29(4):889–901

    Article  Google Scholar 

  127. Gong J, Caldas CH (2010) Computer vision-based video interpretation model for automated productivity analysis of construction operations. J Comput Civ Eng 24(3):252–263

    Article  Google Scholar 

  128. Gong J, Caldas CH (2011) An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Autom Constr 20(8):1211–1226

    Article  Google Scholar 

  129. Brilakis I, Park M-W, Jog G (2011) Automated vision tracking of project related entities. Adv Eng Inform 25(4):713–724

    Article  Google Scholar 

  130. Jog GM, Brilakis IK, Angelides DC (2011) Testing in harsh conditions: tracking resources on construction sites with machine vision. Autom Constr 20(4):328–337

    Article  Google Scholar 

Download references

Funding

Funding was provided by Australian Research Council (Grant No. LP180100222).

Author information

Authors and Affiliations

  1. School of Design and the Built Environment, Curtin University, Bentley, Australia

    Shuyuan Xu

  2. School of Architecture and Built Environment, Deakin University, Geelong, Australia

    Jun Wang & Abdul-Manan Sadick

  3. School of Computing, Engineering and Mathematics, Western Sydney University, Parramatta, Australia

    Wenchi Shou

  4. Department of Infrastructure Engineering, The University of Melbourne, Parkville, Australia

    Tuan Ngo

  5. School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, China

    Xiangyu Wang

  6. Australasian Joint Research Centre for Building Information Modelling, Curtin University, Bentley, Australia

    Xiangyu Wang

Authors
  1. Shuyuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenchi Shou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tuan Ngo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdul-Manan Sadick
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiangyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jun Wang or Xiangyu Wang.

Ethics declarations

Conflict of interest

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, S., Wang, J., Shou, W. et al. Computer Vision Techniques in Construction: A Critical Review. Arch Computat Methods Eng 28, 3383–3397 (2021). https://doi.org/10.1007/s11831-020-09504-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-020-09504-3

Search

Navigation