Extraction of Workers and Heavy Equipment and Muliti-Object Tracking using Surveillance System in Construction Sites |
Cho, Young-Woon
(Construction Engineering and Management Institute, Sahmyook University)
Kang, Kyung-Su (Construction Engineering and Management Institute, Sahmyook University) Son, Bo-Sik (Department of Architectural Engineering, Namseoul University) Ryu, Han-Guk (Department of Architectural, Sahmyook University) |
1 | GitHub: Where the world builds software [Internet]. Deep learning-based Computer Vision Models for PyTorch: GitHub, Inc. 2008 - [cited 2021 Apr 7]. Available from: https://github.com/unerue/boda |
2 | Xuehui A, Li Z, Zuguang L, Chengzhi W, Pengfei L, Zhiwei L. Dataset and benchmark for detecting moving objects in construction sites. Automation in Construction. 2021 Feb;122:103482. https://doi.org/10.1016/j.autcon.2020.103482 DOI |
3 | Kuhn HW. The Hungarian method for the assignment problem. Naval research logistics quarterly. 1955 Mar;2(1-2):83-97. https://doi.org/10.1002/nav.3800020109 DOI |
4 | Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2015 Oct 5; Munich, Germany. MN: The Medical Image Computing and Computer Assisted Intervention Society; 2015. p. 234-41. https://doi.org/10.1007/978-3-319-24574-4_28 DOI |
5 | Angah O, Chen AY. Tracking multiple construction workers through deep learning and the gradient based method with rematching based on multi-object tracking accuracy. Automation in Construction. 2020 Nov;119:103308. https://doi.org/10.1016/j.autcon.2020.103308 DOI |
6 | He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV); 2017 Oct 22-29; Venice, Italy. NJ: Institute of Electrical and Electronics Engineers; 2017. p. 2961-9. https://doi.org/10.1109/ICCV.2017.322 DOI |
7 | Nath ND, Behzadan AH, Paal SG. Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction. 2020 Apr;112:103085. https://doi.org/10.1016/j.autcon.2020.103085 DOI |
8 | Son H, Choi H, Seong H, Kim C. Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks. Automation in Construction. 2019 Mar;99:27-38. https://doi.org/10.1016/j.autcon.2018.11.033 DOI |
9 | Kim D. Occupational accident/injury analysis 2009. Ulsan (Korea): Korea Occupational Safety and Health Agency; 2021 Jan;15-22. Grant No.: 118006 Supported by KOSTAT. |
10 | Heejung. Women who "watch the monitor" [Internet]. Seoul (Korea): Ildaro. 2019 Aug 30 [cited 2021 Apr 7]. Available from: https://ildaro.com/8536 |
11 | Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017 Apr;39(4): 640-51. https://doi.org/10.1109/TPAMI.2016.2572683 DOI |
12 | Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence. 2017 Jun;39(6):1137-49. https://doi.org/10.1109/TPAMI.2016.2577031 DOI |
13 | Guo Y, Xu Y, Li S. Dense construction vehicle detection based on orientation-aware feature fusion convolutional neural network. Automation in Construction. 2020 Apr;112:103124. https://doi.org/10.1016/j.autcon.2020.103124 DOI |
14 | Li Z, Zhou F. FSSD: feature fusion single shot multibox detector. arXiv:1712.00960 [Preprint]. 2017 [cited 2021 Apr 12]. Available from: https://arxiv.org/abs/1712.00960 |
15 | Voigtlaender P, Krause M, Osep A, Luiten J, Sekar BB, Geiger A, Leibe B. Mots: Multi-object tracking and segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). 2019 June 15-20; Long Beach, CA. NJ: Institute of Electrical and Electronics Engineers; 2020. p. 7942-51. https://doi.org/10.1109/CVPR.2019.00813 DOI |
16 | Truong T, Bhatt A, Queiroz L, Lai K, Yanushkevich S. Instance segmentation of personal protective equipment using a multi-stage transfer learning process. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2020 Oct 11-14; Toronto, Canada. NJ: Institute of Electrical and Electronics Engineers; 2017. p.1181-6. https://doi.org/10.1109/SMC42975.2020.9283427 DOI |
17 | Yang Z, Yuan Y, Zhang M, Zhao X, Zhang Y, Tian B. Safety distance identification for crane drivers based on mask R-CNN. Sensors. 2019 Jan;19(12):2789. https://doi.org/10.3390/s19122789 DOI |
18 | GitHub: Where the world builds software [Internet]. Image Polygonal Annotation with Python: GitHub, Inc. 2008 - [cited 2021 Apr 7]. Available from: https://github.com/wkentaro/labelme |
19 | Bolya D, Zhou C, Xiao F, Lee YJ. Yolact: Real-time instance segmentation. 2019 IEEE/CVF International Conference on Computer Vision(ICCV). 2019 Oct 27-Nov 2; Seoul, Korea. NJ: Institute of Electrical and Electronics Engineers; 2020. p.9157-66. https://doi.org/10.1109/ICCV.2019.00925 DOI |
20 | Park Y. Only one person monitors 438 CCTVs [Internet]. Seoul (Korea): Munhwa Ilbo. 2017 Nov 28 [cited 2021 Apr 7]. Available from: http://www.munhwa.com/news/view.html?no=2017112801031627109001 |
21 | Lin TY, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2017 Jul 21-26; Honolulu, HI. NJ: Institute of Electrical and Electronics Engineers; 2017. p. 2117-25. https://doi.org/10.1109/CVPR.2017.106 DOI |
22 | Luo W, Xing J, Milan A, Zhang X, Liu W, Kim TK. Multiple object tracking: A literature review. Artificial Intelligence. 2021 Apr 293:103448. https://doi.org/10.1016/j.artint.2020.103448 DOI |
23 | Bewley A, Ge Z, Ott L, Ramos F, Upcroft B. Simple online and realtime tracking. 2016 IEEE international conference on image processing(ICIP). 2016 Sept 25-28; Phoenix, AZ. NJ: Institute of Electrical and Electronics Engineers; 2016. p. 3464-8. https://doi.org/10.1109/ICIP.2016.7533003 DOI |
24 | Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A. The pascal visual object classes (voc) challenge. International journal of computer vision. 2010 Jun;88(2):303-38. https://doi.org/10.1007/s11263-009-0275-4 DOI |
25 | Lee YJ, Park MW. 3D tracking of multiple onsite workers based on stereo vision. Automation in Construction. 2019 Feb;98:146-59. https://doi.org/10.1016/j.autcon.2018.11.017 DOI |
26 | Kim H. Construction safety innovation plan: Reinforcement of management of vulnerable construction, etc [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport. 2020 Apr 24 [cited 2021 Apr 7]. Available from: http://www.molit.go.kr/USR/NEWS/m_71/dtl.jsp?id=95083805 |
27 | Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollar P, Zitnick CL. Microsoft coco: Common objects in context. European conference on computer vision. 2014 Sep;8693:740-55. https://doi.org/10.1007/978-3-319-10602-1_48 DOI |
28 | LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. Backpropagation applied to handwritten zip code recognition. Neural computation. 1989 Dec;1(4):541-51. https://doi.org/10.1162/neco.1989.1.4.541 DOI |
29 | Dalal N, Triggs B. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2005 Jun 20-25; San Diego, CA. NJ: Institute of Electrical and Electronics Engineers; 2005. p. 886-93. https://doi.org/10.1109/CVPR.2005.177 DOI |
30 | Park MW, Brilakis I. Continuous localization of construction workers via integration of detection and tracking. Automation in Construction. 2016 Dec;72:129-42. https://doi.org/10.1016/j.autcon.2016.08.039 DOI |
31 | Ishioka H, Weng X, Man Y, Kitani K. Single camera worker detection, tracking and action recognition in construction site. Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC); 2020 Oct; Kitakyushu, Japan. FL: International Association for Automation and Robotics in Construction (IAARC); 2020. p. 653-60. https://doi.org/10.22260/ISARC2020/0092 DOI |
32 | Choi M, Choi J. CCTV integrated control center operation status and improvement plan legislative policy report. Seoul, Korea: National Assembly Research Service, NARS; 2019. p. 1-33. |
33 | Zhang Z. Determining the epipolar geometry and its uncertainty: A review. International journal of computer vision. 1998 Mar;27(2):161-95. https://doi.org/10.1023/A:1007941100561 DOI |
34 | Zhao Y, Chen Q, Cao W, Yang J, Xiong J, Gui G. Deep learning for risk detection and trajectory tracking at construction sites. IEEE Access; 2019 Mar;7:30905-12. https://doi.org/10.1109/ACCESS.2019.2902658 DOI |
35 | Redmon J, Farhadi A. Yolov3: An incremental improvement. arXiv:1804:02767 [Preprint]. 2018 [cited 2021 Apr 8]. Available from: https://arxiv.org/abs/1804.02767 |
36 | Kalman RE. A new approach to linear filtering and prediction problems. 1960 Mar;82(1):35-45. https://doi.org/10.1115/1.3662552 DOI |
37 | Wojke N, Bewley A, Paulus D. Simple online and realtime tracking with a deep association metric. 2017 IEEE international conference on image processing(ICIP). 2017 Sep 17-20; Beijing, China. NJ: Institute of Electrical and Electronics Engineers; 2018. p. 3645-9. https://doi.org/10.1109/ICIP.2017.8296962 DOI |
38 | Leal-Taixe L, Milan A, Reid I, Roth S, Schindler K. Motchallenge 2015: Towards a benchmark for multi-target tracking. arXiv:1504.01942 [Preprint]. 2015 [cited 2021 Apr 8]. Available from: https://arxiv.org/abs/1504.01942 |
39 | Milan A, Leal-Taixe L, Reid I, Roth S, Schindler K. MOT16: A benchmark for multi-object tracking. arXiv:1603.00831 [Preprint]. 2016 [cited 2021 Apr 8]. Available from: https://arxiv.org/abs/1603.00831 |