ORGANIZACION DE LAS PRUEBAS OFICIALES DE LA F.I.E
E) PRUEBAS DE LA COPA DEL MUNDO § 1. Generalidades
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CONTRIBUTION
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Based on the test results of this proposed method, several limitations were noted, 714
limitations that could be improved on the future. First, with the current detection template, 715
the method can only detect people that are standing or walking. Individuals in other 716
positions (e.g. crouching down, bending, and sitting) cannot be detected successfully Those 717
who are bending or sitting can only be detected when they change their posture to being 718
standing. This missed detection problem arose because the detection template adopted for 719
this version of the method was trained using images of standing workers. As a solution, we 720
can extend the detection template by training it with images of workers in different 721
postures. Another solution would be to create different detection models for each posture. 722
The \These two solutions will be investigated for their effectiveness and a more generalized 723
method for detecting construction workers with different postures will be developed in 724
future work. 725
Second, the proposed method relies on the spatial and geometric relation between 726
the recognition windows of people and hardhats to perform the people-hardhat matching. 727
Closely related to the first problem, the matching process between the hardhats and human 728
bodies gives a negative result when individuals inside the construction site not standing or 729
not walking. If people have other postures, the matching parameters proposed here will 730
have to be correspondingly adapted. For example, if a construction worker is crouching 731
down or bending, the position of the hardhat might be in the left-top area of the worker's 732
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recognition region, as illustrated in (Figure 5-1). However, the exact matching parameters 733
cannot be determined until the recognition of construction workers with different postures 734
is implemented. 735
736
Figure 5-1: Potential spatial and geometric relationship between a hardhat and a 737
worker not standing or walking 738
Third, one of the major limitations that affect the performance of the proposed 739
method is occlusion, a problem similar to that of other vision-based methods. If any objects 740
partially or fully occlude a worker, that worker cannot be detected or monitored with the 741
method. The method can detect the workers when they appear clearly in the camera’s view. 742
Installing cameras inside a construction site at a certain height level in order to reduce the 743
chances of occlusions and guarantee the effectiveness of the proposed method could be an 744
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effective way to solve this problem. Also, placing multiple cameras would make it possible 745
to cover a larger area of a construction site. 746
There is another issue, related to the proposed method’s use of background 747
subtraction to reduce the video processing time. This step enables the method to only detect 748
moving workers, hence workers without movement are not identified. Static workers were 749
considered as a part of the background and were subtracted during the background 750
subtraction. However, there are opportunities to detect static workers. For example, turning 751
off the background subtractions and considering the whole field of the camera view as the 752
foreground will enable the method to detect static workers, but this will slow the creation 753
of a safety alert. Static workers could also be detected when they first enter a camera’s 754
view. Therefore, the integration of the detection and tracking of construction workers will 755
provide another way to continuously monitor workers even if they are static. 756
The automated recognition of workers without hardhats accomplished through this 757
research work provides an automated and remote way to monitor and control the safety of 758
the workers inside the construction site. In doing so, a matching process performed between 759
each detected hardhat and its corresponding human body. When the hardhats didn’t locate 760
in one of the expected regions shown in figure (3-13) the safety alert issued. In some cases 761
the safety alert issued wrongly, as the hardhat didn’t located in the exact region. In order 762
to solve this problem other methods will be examined in the future work such as The 763
Artificial Neural Network. The Artificial Neural Network could be used in the matching 764
and detection processes for detecting the workers without hardhats. The ANN could 765
decrees the time of creating the detection models as it use a smaller numbers of training 766
53
images compared with the used method. Also the ability of detection the workers in 767
difference bosses with different location of the hardhats could be examine using ANN and 768
could give an acceptable result. 769 770 771 772 773 774
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CONCLUSION
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The construction sector is one of the most dangerous job sectors, and it also 776
employs a large number of people, often with different levels of training. Governments 777
have established safety regulations and procedures to increase construction site safety, but 778
they are not enough. Construction workers may slip up and not always follow the safety 779
requirements due to fatigue, distractions, carelessness, etc. Therefore, it is very important 780
to ensure that these safety regulations and procedures are followed inside any construction 781
site, all of the time. 782
Currently, it is inspectors who are responsible for verifying safety regulations at 783
construction sites. An inspector monitors and controls the safety at a given site. This thesis 784
proposed a novel, vision-based method to automatically check whether people at 785
construction sites are wearing hardhats. This method is comprised of four parts: human 786
body detection, hardhat detection, matching and then the issuing safety alerts when 787
construction workers are not wearing hardhats. The method is expected to facilitate and 788
automatically monitor the work of construction site safety inspectors. The method has been 789
tested with real site videos. According to the test results, the safety alerts were successfully 790
issued when construction workers were not wearing hardhats with an overall precision of 791
94.3% and a recall of 89.4%. The second hardhat detection scheme gave a higher safety 792
alert precision and recall, indicating that the worksite safety in terms of hardhat-wearing 793
could be monitored with live streaming or time-lapse videos Maximizing the hardhat 794
detection recall played an important role in improving the precision for issuing safety alerts 795
due to not wearing hardhats. 796
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Reverences:
798
Abeid, J. and D. Arditi (2002). "Time-Lapse Digital Photography Applied to Project 799
Management." Journal of Construction Engineering and Management 128(6): 530-535. 800
801
Aguilar, G. E. and K. N. Hewage (2013). "IT based system for construction safety 802
management and monitoring: C-RTICS< sup> 2</sup>." Automation in Construction 35: 803
217-228. 804
805
Bohn, J. and J. Teizer (2010). "Benefits and Barriers of Construction Project Monitoring 806
Using High-Resolution Automated Cameras." Journal of Construction Engineering and 807
Management 136(6): 632-640. 808
809
Bureau Of Labor Statistics, U. D. o. L. (2010). Nonfatal Occupational Injuries And 810
Illnesses Requiring Days Away From Work, 2009 USA, USDL: 23. 811
812
Canada Statistics (2008). Construction work Injuries, Canada Statistics 813
814
Canada Statistics (2011). Construction work Injuries, Canada Statistics 815
816
Carbonari, A., et al. (2011). "A proactive system for real-time safety management in 817
construction sites." Automation in Construction 20(6): 686-698. 818
819
CBC News (2011). Surveyor killed in Quebec road construction accident. CBC News. 820
Montreal, CBC News. 821
822
Cheng, T. and J. Teizer (2013). "Real-time resource location data collection and 823
visualization technology for construction safety and activity monitoring applications." 824
Automation in Construction 34: 3-15. 825
826
Chi, S. and C. H. Caldas (2011). "Automated object identification using optical video 827
cameras on construction sites." Computer‐Aided Civil and Infrastructure Engineering 828
26(5): 368-380. 829
830
Dalal, N. and B. Triggs (2005). Histograms of oriented gradients for human detection. 831
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society 832
Conference on, IEEE. 833
834
Felzenszwalb, P. F., et al. (2010). "Object detection with discriminatively trained part- 835
based models." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32(9): 836
1627-1645. 837
838
German, S., et al. (2013). "Machine Vision-Enhanced Postearthquake Inspection." Journal 839
of Computing in Civil Engineering 27(6): 622-634. 840
56 841
Golparvar-Fard, M., et al. (2009). Monitoring of construction performance using daily 842
progress photograph logs and 4d as-planned models. ASCE International Workshop on 843
Computing in Civil Engineering. 844
845
Green, L. and G. Tominack (2012). "Real-time Proactive Safety in Construction." POWER 846
156(1): 62-65. 847
848
Gualdi, G., et al. (2009). Covariance descriptors on moving regions for human detection in 849
very complex outdoor scenes. Distributed Smart Cameras, 2009. ICDSC 2009. Third 850
ACM/IEEE International Conference on, IEEE. 851
852
Gualdi, G., et al. (2011). "Contextual information and covariance descriptors for people 853
surveillance: an application for safety of construction workers." Journal on Image and 854
Video Processing 2011: 9. 855
856
Han, S., et al. (2013). "Empirical assessment of a RGB-D sensor on motion capture and 857
action recognition for construction worker monitoring." Visualization in Engineering 1(1): 858
1-13. 859
860
Han, S. and S. Lee (2013). "A vision-based motion capture and recognition framework for 861
behavior-based safety management." Automation in Construction 35: 131-141. 862
863
Idiz, S. (2014). Turkish workers face perilous conditions. ALMONTOR. 864
865
Jog, G., et al. (2011). "Truck-face recognition using semantic texton forests." 866
867
Kelm, A., et al. (2013). "Mobile passive Radio Frequency Identification (RFID) portal for 868
automated and rapid control of Personal Protective Equipment (PPE) on construction 869
sites." Automation in Construction 36: 38-52. 870
871
Liaw, Y., et al. (2012). Learning Assessment Strategies for an 872
Educational Construction Safety Video Game. Construction Research Congress. 1: 10. 873
874
McFarlane, N. J. and C. P. Schofield (1995). "Segmentation and tracking of piglets in 875
images." Machine Vision and Applications 8(3): 187-193. 876
877
Memarzadeh, M., et al. (2013). "Automated 2D detection of construction equipment and 878
workers from site video streams using histograms of oriented gradients and colors." 879
Automation in Construction 32: 24-37. 880
881
OSHA (2014). Occupational Safety & Health Administration. 882
883
Park, M.-W. and I. Brilakis (2012). "Construction worker detection in video frames for 884
initializing vision trackers." Automation in Construction 28: 15-25. 885
57
Park, M.-W., et al. (2011). "Three-dimensional tracking of construction resources using an 887
on-site camera system." Journal of Computing in Civil Engineering 26(4): 541-549. 888
889
Quebec, G. o. (2014). Safety Code for the construction industry. 890
891
Rezazadeh Azar, E. and B. McCabe (2011). "Automated visual recognition of dump trucks 892
in construction videos." Journal of Computing in Civil Engineering 26(6): 769-781. 893
894
Ruff, T., et al. (2011). "Machine-related injuries in the US mining industry and priorities 895
for safety research." International journal of injury control and safety promotion 18(1): 11- 896
20. 897 898
Ruff, T. M. (2007). Recommendations for evaluating and implementing proximity warning 899
systems on surface mining equipment, Department of Health and Human Services, Public 900
Health Serivce, Centers for Disease Control and Prevention, National Institute for 901
Occupational Safety and Health, Spokane Research Laboratory. 902
903
Shotton, J., et al. (2008). Semantic texton forests for image categorization and 904
segmentation. Computer vision and pattern recognition, 2008. CVPR 2008. IEEE 905
Conference on, IEEE. 906
907
Steele, J., et al. (2003). "Stereo images for object detection in surface mine safety 908
applications." Colorado School of Mines, Western Mining Resource Center Tech Report 909
TR20030109. 910
911
Teizer, J., et al. (2010). "Autonomous pro-active real-time construction worker and 912
equipment operator proximity safety alert system." Automation in Construction 19(5): 630- 913
640. 914
915
Wang, J., et al. (2011). Computer Vision for Multimedia Applications: Methods and 916
Solutions, IGI Global. 917
918
Weerasinghe, I. P. and J. Y. Ruwanpura (2009). Automated data acquisition system to 919
assess construction worker performance. Proceedings of Construction Research Congress 920
2009. 921
922
Weerasinghe, I. P. and J. Y. Ruwanpura (2010). Automated multiple objects tracking 923
system (AMOTS). Construction Research Congress 2010. 924
925
Weerasinghe, I. P. and R. Tharindu (2013). "Automated Construction Worker Performance 926
and Tool-time Measuring Model Using RGB Depth Camera and Audio Microphone Array 927
System." 928
929
WorkSafeBC (2014). Top 3 Accident Types - Falls. Canada, WorkSafeBC. 930
58
Zhang, H., et al. (2012). "Efficient safety information retrieval on construction sites: a 932
preliminary methodology." 933
934
Zhu, Z., et al. (2011). "Visual retrieval of concrete crack properties for automated post- 935
earthquake structural safety evaluation." Automation in Construction 20(7): 874-883. 936 937 938 939 940 941 942 943