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Journal of the Korean Society of Civil Engineers

Journal of the Korean Society of Civil Engineers

ISO Journal TitleKSCE J. Civ. Environ. Eng. Res.
  • Open Access, Bi-monthly

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  3. 2019-10 (v.39 n.5)
  4. 10.12652/Ksce.2019.39.5.0631
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1 
Azar, E. R. and McCabe, B. (2012a). "Automated visual recognition of dump trucks in construction videos." Journal of Computing in Civil Engineering, Vol. 26, No. 6, pp. 769-781.DOI
2 
Azar, E. R. and McCabe, B. (2012b). "Part based model and spatial-temporal reasoning to recognize hydraulic excavators in construction images and videos." Automation in Construction, Vol. 24, pp. 194-202.DOI
3 
Azar, E. R., Dickinson, S. and McCabe, B. (2013). "Server-Customer interaction tracker: computer vision-based system to estimate dirt-loading cycles." Journal of Construction Engineering and Management, Vol. 139, No. 7, pp. 785-794.DOI
4 
Cai, J., Zhang, Y. and Cai, H. (2019). "Two-step long short-term memory method for identifying construction activities through positional and attentional cues." Automation in Construction, Vol. 106, p. 102886.DOI
5 
Chi, S. and Caldas, C. H. (2011). "Automated object identification using optical video cameras on construction sites." Computer-Aided Civil and Infrastructure Engineering, Vol. 26, No. 5, pp. 368-380.DOI
6 
Chi, S. and Caldas, C. H. (2012). "Image-based safety assessment: automated spatial safety risk identification of earthmoving and surface mining activities." Journal of Construction Engineering and Management, Vol. 138, No. 3, pp. 341-351.DOI
7 
Chi, S., Caldas, C. H. and Kim, D. Y. (2009). "A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques." Computer-Aided Civil and Infrastructure Engineering, Vol. 24, No. 3, pp. 199-211.DOI
8 
Chung, S. (2018). Bridge damage factor recognition from inspection reports using active recurrent neural network, Master Thesis, Seoul National University.
9 
Fang, W., Ding, L., Zhong, B., Love, P. E. D. and Luo, H. (2018). "Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach." Advanced Engineering Informatics, Vol. 37, pp. 139-149.DOI
10 
Golparvar-Fard, M., Heydarian, A. and Niebles, J. C. (2013). "Vision- based action recognition of earthmoving equipment using spatio- temporal features and support vector machine classifiers." Advanced Engineering Informatics, Vol. 27, No. 4, pp. 652-663.DOI
11 
Gong, J. and Caldas, C. H. (2010). "Computer vision-based video interpretation model for automated productivity analysis of construction operations." Journal of Computing in Civil Engineering, Vol. 24, No. 3, pp. 252-263.DOI
12 
Han, S. U., Achar, M., Lee, S. H. and Peña-Mora, F. (2013). "Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring." Visualization in Engineering, Vol. 1, No. 1, pp. 1-13.DOI
13 
Han, S. U., Lee, S. H. and Peña-mora, F. (2014). "Vision-based detection of unsafe actions of a construction worker: case study of ladder climbing." Journal of Computing in Civil Engineering, Vol. 27, No. 6, pp. 635-644.DOI
14 
Kim, H. J., Kim, H. K., Hong, Y. W. and Byun, H. R. (2018b). "Detecting construction equipment using a region-based fully convolutional network and transfer learning." Journal of Computing in Civil Engineering, Vol. 32, No. 2, p. 04017082.DOI
15 
Kim, H. J., Kim, K. N. and Kim, H. K. (2016). "Vision-based object-centric safety assessment using fuzzy inference: monitoring struck-by accidents with moving objects." Journal of Computing in Civil Engineering, Vol. 30, No. 4, p. 04015075.DOI
16 
Kim, J. W. and Chi, S. H. (2017). "Adaptive detector and tracker on constrution sites using functional integration and online learning." Journal of Computing in Civil Engineering, Vol. 31, No. 5, p. 04017026.DOI
17 
Kim, J. W. and Chi, S. H. (2019). "Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles." Automation in Construction, Vol. 104, pp. 255-264.DOI
18 
Kim, J. W., Chi, S. H. and Seo, J. W. (2018a). "Interaction analysis for vision-based activity identification of earthmoving excavators and dump trucks." Automation in Construction, Vol. 87, pp. 297-308.DOI
19 
Kim, J., Ham, Y. J., Chung, Y. H. and Chi, S. H. (2019). "Systematic camera placement framework for operation-level visual monitoring on construction jobsites." Journal of Construction Engineering and Management, Vol. 145, No. 4, p. 04019019.DOI
20 
Korea Construction Technology Promotion Act (2016). Enforcement decree article 98 and 99, statutes of the Republic of Korea.
21 
Luo, X., Li, H., Cao, D., Dai, F., Seo, J. and Lee, S. (2018). "Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks." Journal of Computing in Civil Engineering, Vol. 32, No. 3, p. 04018012.DOI
22 
Memarzadeh, M., Golparvar-Fard, M. and Niebles, J. C. (2013). "Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors." Automation in Construction, Vol. 32, pp. 24-37.DOI
23 
Park, M. W. and Brilakis, I. (2012). "Construction worker detection in video frames for initializing vision trackers." Automation in Construction, Vol. 28, pp. 15-25.DOI
24 
Park, M. W. and Brilakis, I. (2016). "Continuous localization of construction workers via integration of detection and tracking." Automation in Construction, Vol. 72, pp. 129-142.DOI
25 
Park, M. W., Elsafty, N. and Zhu, Z. (2015). "Hardhat-wearing detection for enhancing on-site safety of construction workers." Journal of Construction Engineering and Management, Vol. 141, No. 9, p. 04015024.DOI
26 
Ren, S., He, K., Girshick, R. and Sun, J. (2017). "Faster R-CNN: towards real-time object detection with region proposal networks." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149.DOI
27 
Settles, B. (2010). Active learning literature survey. Computer Science Technical Report 1648, University of Wisconsin-Madison.
28 
Soltani, M. M., Zhu, Z. and Hammad, A. (2017). "Skeleton estimation of excavator by detecting its parts." Automation in Construction, Vol. 82, pp. 1-15.DOI
29 
Son, H. J., Choi, H. C., Seong, H. W. and Kim, C. W. (2019). "Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks." Automation in Construction, Vol. 99, pp. 27-38.DOI
30 
Yang, J., Vela, P., Teizer, J. and Shi, Z. (2014). "Vision-based tower crane tracking for understanding construction activity." Journal of Computing in Civil Engineering, Vol. 28, No. 1, pp. 103-112.DOI
31 
Zhu, Z., Ren, X. and Chen, Z. (2016). "Visual tracking of construction jobsite workforce and equipment with particle filtering." Journal of Computing in Civil Engineering, Vol. 30, No. 6, pp. 1-15.DOI
32 
Zou, J. and Kim, H. (2007). "Using hue, saturation, and value color space for hydraulic excavator idle time analysis." Journal of Computing in Civil Engineering, Vol. 21, No. 4, pp. 238-246.DOI