Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction

[1]  Peter E. D. Love,et al.  A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network , 2019, Adv. Eng. Informatics.

[2]  Peter E.D. Love,et al.  Reduce rework, improve safety: an empirical inquiry into the precursors to error in construction , 2018 .

[3]  Steven A. Freeman,et al.  Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators , 2019, Applied Sciences.

[4]  Xiaochun Luo,et al.  Vision-based detection and visualization of dynamic workspaces , 2019, Automation in Construction.

[5]  Peter E.D. Love,et al.  Real-time smart video surveillance to manage safety: A case study of a transport mega-project , 2020, Adv. Eng. Informatics.

[6]  Xiaochun Luo,et al.  Recognizing Diverse Construction Activities in Site Images via Relevance Networks of Construction-Related Objects Detected by Convolutional Neural Networks , 2018, J. Comput. Civ. Eng..

[7]  Patricio A. Vela,et al.  Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes , 2017, Comput. Aided Civ. Infrastructure Eng..

[8]  Zhi Chen,et al.  Visual Tracking of Construction Jobsite Workforce and Equipment with Particle Filtering , 2016, J. Comput. Civ. Eng..

[9]  Mani Golparvar-Fard,et al.  End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level , 2019, Automation in Construction.

[10]  Bo Xiao,et al.  Two-Dimensional Visual Tracking in Construction Scenarios: A Comparative Study , 2018, J. Comput. Civ. Eng..

[11]  Peter E.D. Love,et al.  Falls from heights: A computer vision-based approach for safety harness detection , 2018, Automation in Construction.

[12]  Andrew Shaw,et al.  Safety climate and the theory of planned behavior: towards the prediction of unsafe behavior. , 2010, Accident; analysis and prevention.

[13]  Qiang Wang,et al.  Fast Online Object Tracking and Segmentation: A Unifying Approach , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Peter E. D. Love,et al.  Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach , 2018, Adv. Eng. Informatics.

[15]  Xiaochun Luo,et al.  Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network , 2018, Comput. Aided Civ. Infrastructure Eng..

[16]  Sangyoon Chin,et al.  Machine learning predictive model based on national data for fatal accidents of construction workers , 2020 .

[17]  Silvio Savarese,et al.  Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Silvio Savarese,et al.  Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Jimmie Hinze,et al.  Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system , 2010 .

[20]  Onur Behzat Tokdemir,et al.  Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks , 2020 .

[21]  Tak Wing Yiu,et al.  Predicting safety behavior in the construction industry: development and test of an integrative model , 2016 .

[22]  Dani Lischinski,et al.  Crowds by Example , 2007, Comput. Graph. Forum.

[23]  Soojin Cho,et al.  Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model , 2020, Applied Sciences.

[24]  M. Dixon-Woods,et al.  The problem with root cause analysis , 2016, BMJ Quality & Safety.

[25]  Hubo Cai,et al.  Multitask Learning Method for Detecting the Visual Focus of Attention of Construction Workers , 2021, Journal of Construction Engineering and Management.

[26]  Kinam Kim,et al.  Vision-Based Object-Centric Safety Assessment Using Fuzzy Inference: Monitoring Struck-By Accidents with Moving Objects , 2016, J. Comput. Civ. Eng..

[27]  Blair H. Sheppard,et al.  The Theory of Reasoned Action: A Meta-Analysis of Past Research with Recommendations for Modifications and Future Research , 1988 .

[28]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[29]  Chong Luo,et al.  A Twofold Siamese Network for Real-Time Object Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[30]  Vineet R. Kamat,et al.  Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection , 2019 .

[31]  Yizhou Wang,et al.  Video Object Segmentation by Learning Location-Sensitive Embeddings , 2018, ECCV.

[32]  Bernt Schiele,et al.  Learning Video Object Segmentation from Static Images , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Huchuan Lu,et al.  Robust Object Tracking via Sparse Collaborative Appearance Model , 2014, IEEE Transactions on Image Processing.

[34]  Peter E.D. Love,et al.  A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory , 2018 .

[35]  Peter E.D. Love,et al.  Computer vision for behaviour-based safety in construction: A review and future directions , 2020, Adv. Eng. Informatics.

[36]  P. Love,et al.  Computer Vision and Deep Learning to Manage Safety in Construction: Matching Images of Unsafe Behavior and Semantic Rules , 2021, IEEE Transactions on Engineering Management.

[37]  I. Ajzen.  The theory of planned behavior , 1991 .

[38]  Ohay Angah,et al.  Tracking multiple construction workers through deep learning and the gradient based method with re-matching based on multi-object tracking accuracy , 2020 .

[39]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[40]  Jiannan Cai,et al.  Robust Hybrid Approach of Vision-Based Tracking and Radio-Based Identification and Localization for 3D Tracking of Multiple Construction Workers , 2020 .

[41]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Amin Hammad,et al.  Predicting movements of onsite workers and mobile equipment for enhancing construction site safety , 2016 .

[43]  P. Love,et al.  Unearthing the nature and interplay of quality and safety in construction projects: an empirical study , 2018 .

[44]  Yang Yang,et al.  Instance-level recognition and quantification for concrete surface bughole based on deep learning , 2019, Automation in Construction.

[45]  Fran Ackermann,et al.  The nature and severity of workplace injuries in construction: engendering operational benchmarking , 2019, Ergonomics.

[46]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Hiam Khoury,et al.  Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications , 2018 .

[49]  Kumar Neeraj Jha,et al.  Neural Network Model for the Prediction of Safe Work Behavior in Construction Projects , 2015 .

[50]  Peter E.D. Love,et al.  Computer vision applications in construction safety assurance , 2020 .

[51]  Zhou Li,et al.  Dataset and benchmark for detecting moving objects in construction sites , 2021 .

[52]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Peter E.D. Love,et al.  Rework, Failures, and Unsafe Behavior: Moving Toward an Error Management Mindset in Construction , 2020, IEEE Transactions on Engineering Management.

[54]  Gary A. Atkinson,et al.  Image segmentation of underfloor scenes using a mask regions convolutional neural network with two-stage transfer learning , 2020 .

[55]  Jochen Teizer,et al.  Visibility-related fatalities related to construction equipment , 2011 .

[56]  Ronan Collobert,et al.  Learning to Segment Object Candidates , 2015, NIPS.

[57]  Luc Van Gool,et al.  A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).