Tracking multiple workers on construction sites using video cameras
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O. Arif, P.A. Vela, Kernel covariance image region description for object tracking, in: IEEE International Conference on Image Processing, 2009.
Navon, 2007, Assessing research issues in automated project performance control (APPC), Automation in Construction, 16, 474, 10.1016/j.autcon.2006.08.001
Golparvar-Fard, 2009
C. Gordon, F. Boukamp, D. Huber, E. Latimer, K. Park, B. Akinci, Combining reality capture technologies for construction defect detection: a case study, in: EIA9: E-Activities and Intelligent Support in Design and the Built Environment, 9th EuropIA International Conference, 2003, pp. 99–108.
Kamat, 2005, Large-scale dynamic terrain in three-dimensional construction process visualizations, Journal of Computing in Civil Engineering, 19, 160, 10.1061/(ASCE)0887-3801(2005)19:2(160)
Trucco, 2004, A framework for automatic progress assessment on construction sites using computer vision, International Journal of IT in Architecture, Engineering and Construction, 2, 147
T. Lukins, E. Trucco, Towards automated visual assessment of progress in construction projects, in: Proceedings of the British Machine Vision Conference, Coventry, 2007, pp. 142–151.
N. Choi, H. Son, C. Kin, Rapid 3D object recognition for automatic project progress monitoring using a stereo vision system, in: The 25th International Symposium on Automation and Robotics in Construction, 2009.
Peddi, 2009, Development of human pose analyzing algorithms for the determination of construction productivity in real-time, 11
J. Gong, C.H. Caldas, A computer vision based video interpretation model for automated productivity analysis of construction operations, Journal of Computing in Civil Engineering (2009) preprint.
F. Cordova, I. Brilakis, On-site 3D vision tracking of construction personnel, in: Conference of the International Group for Lean Construction Management, 2008, pp. 809–820.
Teizer, 2009, Personnel tracking on construction sites using video cameras, Advanced Engineering Informatics, 23, 452, 10.1016/j.aei.2009.06.011
Yilmaz, 2007, Object tracking: a survey, ACM Computing Surveys, 38, 1
Enzweiler, 2009, Monocular pedestrian detection: Survey and experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 2179, 10.1109/TPAMI.2008.260
A. Elgammal, L. Davis, Probabilistic framework for segmenting people under occlusion, in: International Conference on Computer Vision, vol. 1, 2001, pp. 781–788.
F. Porikli, O. Tuzel, P. Meer, Covariance tracking using model update based on Lie algebra, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 728–735.
O. Arif, P. Vela, Non-rigid object localization and segmentation using eigenspace representation, in: IEEE International Conference on Computer Vision, 2009.
Scholkopf, 1998, Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, 10, 1299, 10.1162/089976698300017467
Comaniciu, 2003, Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 564, 10.1109/TPAMI.2003.1195991
B. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in: Proceedings of the DARPA Image Understanding Workshop, 1981, pp. 121–130.
Tipping, 1999, Probabilistic principal component analysis, Journal of the Royal Statistical Society Series B (Statistical Methodology), 61, 611, 10.1111/1467-9868.00196
J. Yang, P.A. Vela, Z. Shi, J. Teizer, Probabilistic multiple people tracking through complex situations, in: 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2009.