Three-Dimensional Tracking of Construction Resources Using an On-Site Camera System

Vision trackers have been proposed as a promising alternative for tracking at large-scale, congested construction sites. They provide the location of a large number of entities in a camera view across frames. However, vision trackers provide only two-dimensional (2D) pixel coordinates, which are not adequate for construction applications. This paper proposes and validates a method that overcomes this limitation by employing stereo cameras and converting 2D pixel coordinates to three-dimensional (3D) metric coordinates. The proposed method consists of four steps: camera calibration, camera pose estimation, 2D tracking, and triangulation. Given that the method employs fixed, calibrated stereo cameras with a long baseline, appropriate algorithms are selected for each step. Once the first two steps reveal camera system parameters, the third step determines 2D pixel coordinates of entities in subsequent frames. The 2D coordinates are triangulated on the basis of the camera system parameters to obtain 3D coordinates. The methodology presented in this paper has been implemented and tested with data collected from a construction site. The results demonstrate the suitability of this method for on-site tracking purposes.

[1]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[2]  Burcu Akinci,et al.  Field Trials of RFID Technology for Tracking Pre-Fabricated Pipe Spools , 2004 .

[3]  Silvio Savarese,et al.  Application of D4AR - A 4-Dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication , 2009, J. Inf. Technol. Constr..

[4]  Burcu Akinci,et al.  Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS , 2007 .

[5]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[6]  Burcu Akinci,et al.  Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects , 2006 .

[7]  Kenichi Kanatani,et al.  Triangulation from Two Views Revisited: Hartley-Sturm vs. Optimal Correction , 2008, BMVC.

[8]  Jochen Teizer,et al.  Rapid Automated Monitoring of Construction Site Activities Using Ultra-Wide Band , 2007 .

[9]  Jie Gong,et al.  Data processing for real-time construction site spatial modeling , 2008 .

[10]  Richard I. Hartley,et al.  In Defense of the Eight-Point Algorithm , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Armin Gruen,et al.  Fundamentals of videogrammetry — A review , 1997 .

[12]  R.J. Fontana,et al.  Recent system applications of short-pulse ultra-wideband (UWB) technology , 2004, IEEE Transactions on Microwave Theory and Techniques.

[13]  Naruo Kano,et al.  APPLICATION OF LOCATION INFORMATION BY STEREO CAMERA IMAGES TO PROJECT PROGRESS MONITORING , 2007 .

[14]  Ioannis K. Brilakis,et al.  Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features , 2011, Adv. Eng. Informatics.

[15]  Patricio A. Vela,et al.  Comparison of Camera Motion Estimation Methods for 3D Reconstruction of Infrastructure , 2011 .

[16]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  J. Barney,et al.  Commercialization of an ultra wideband precision asset location system , 2003, IEEE Conference on Ultra Wideband Systems and Technologies, 2003.

[18]  Carlos H. Caldas,et al.  Integration of Automated Data Collection Technologies for Real-Time Field Materials Management , 2004 .

[19]  Stefan Fuchs,et al.  Multipath Interference Compensation in Time-of-Flight Camera Images , 2010, 2010 20th International Conference on Pattern Recognition.

[20]  Ioannis Brilakis,et al.  Comparative study of vision tracking methods for tracking of construction site resources , 2011 .

[21]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Hongdong Li,et al.  Five-Point Motion Estimation Made Easy , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[23]  Niko Sünderhauf,et al.  COMPARING SEVERAL IMPLEMENTATIONS OF TWO RECENTLY PUBLISHED FEATURE DETECTORS , 2007 .

[24]  Ioannis K. Brilakis,et al.  Automated vision tracking of project related entities , 2011, Adv. Eng. Informatics.

[25]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Roland Siegwart,et al.  Results on Range Image Segmentation for Service Robots , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[27]  Zhongke Shi,et al.  Tracking multiple workers on construction sites using video cameras , 2010, Adv. Eng. Informatics.

[28]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[29]  Joachim Denzler,et al.  Experimental Evaluation of Relative Pose Estimation Algorithms , 2008, VISAPP.

[30]  Jie Gong,et al.  Computer Vision-Based Video Interpretation Model for Automated Productivity Analysis of Construction Operations , 2010 .

[31]  Carlos H. Caldas,et al.  Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources , 2007 .

[32]  Hyojoo Son,et al.  3D structural component recognition and modeling method using color and 3D data for construction progress monitoring , 2010 .

[33]  Philip H. S. Torr,et al.  Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting , 2002, International Journal of Computer Vision.