A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

作者: Lieyun Ding , Weili Fang , Hanbin Luo , Peter E.D. Love , Botao Zhong

DOI: 10.1016/J.AUTCON.2017.11.002

关键词:

摘要: … a consequence of unsafe behavior. If unsafe behavior can be reduced or even prevented, then safety performance will naturally improve. According to Fam, et al. [9] unsafe behavior is …

参考文章(55)
Dan Petersen, H. W. Heinrich, Nestor R. Roos, Industrial accident prevention : a safety management approach Tokyo : McGraw-Hill. ,(1980)
Shawna J Perry, Robert L Wears, Rollin J Fairbanks, None, Handbook of human factors and ergonomics in health care and patient safety CRC Press. ,(2006) , 10.1201/9781482269505
Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici, Beyond short snippets: Deep networks for video classification computer vision and pattern recognition. pp. 4694- 4702 ,(2015) , 10.1109/CVPR.2015.7299101
JoonOh Seo, SangUk Han, SangHyun Lee, Hyoungkwan Kim, Computer vision techniques for construction safety and health monitoring Advanced Engineering Informatics. ,vol. 29, pp. 239- 251 ,(2015) , 10.1016/J.AEI.2015.02.001
Rafiq M. Choudhry, Behavior-based safety on construction sites: a case study. Accident Analysis & Prevention. ,vol. 70, pp. 14- 23 ,(2014) , 10.1016/J.AAP.2014.03.007
Wenkui Ding, Xiubo Geng, Xu-Dong Zhang, Learning to Rank from Noisy Data ACM Transactions on Intelligent Systems and Technology. ,vol. 7, pp. 1- 21 ,(2015) , 10.1145/2576230
Zhenhua Zhu, Ioannis Brilakis, Concrete Column Recognition in Images and Videos Journal of Computing in Civil Engineering. ,vol. 24, pp. 478- 487 ,(2010) , 10.1061/(ASCE)CP.1943-5487.0000053
Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu, 3D Convolutional Neural Networks for Human Action Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 35, pp. 221- 231 ,(2013) , 10.1109/TPAMI.2012.59
Man-Woo Park, Atefe Makhmalbaf, Ioannis Brilakis, Comparative study of vision tracking methods for tracking of construction site resources Automation in Construction. ,vol. 20, pp. 905- 915 ,(2011) , 10.1016/J.AUTCON.2011.03.007