Construction Worker Detection and Tracking in Bird's-Eye View Camera Images

Construction sites are continuously changing environments where construction workers have to adapt to dynamic situations while executing their work tasks safely and efficiently. Simultaneously, they are surrounded by heavy constructionmachinery andmassive crane loadswhich they have to be aware of at any time. Frequent interruptions may often lead to a loss in productivity and also causes hazardous conditions or even incidents, injuries or fatalities. Tracking workers’ paths on site can be used to approach these issues. Recorded tracks can be used to identify close calls of inexperienced or distracted workers. To date, pedestrian workers may participate in customized trainings in order to overcome individual deficits. Machine operators can be assisted tomitigate hazardous situations by warning them from construction workers approaching their machines. Since surveillance cameras are already existent on most construction sites, a video-based detection and tracking system can be implemented at low costs. Relying on video streams, the detection of workers becomes similar to pedestrian detection. Some effort has already been made to elaborate those methods to the needs of construction worker detection. However, in contrast to the frontal view supposed in most pedestrian detection approaches, cameras on construction sites commonly provide oblique or bird’s-eye view perspectives. This complicates the detection task as most body parts of a worker are occluded. Hence, we evaluate the applicability of pedestrian detection approaches in terms of the camera settings at hand. Ensuing, we propose a concept for the detection and tracking of construction workers which allows to improve the productivity and safety on construction

[1]  Junjie Yan,et al.  The Fastest Deformable Part Model for Object Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Yann LeCun,et al.  Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Xiaogang Wang,et al.  A discriminative deep model for pedestrian detection with occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Aviad Shapira,et al.  Vision System for Tower Cranes , 2008 .

[6]  E. Rückert Detecting Pedestrians by Learning Shapelet Features , 2007 .

[7]  S M Kisner,et al.  Machinery-related fatalities in the construction industry. , 1997, American journal of industrial medicine.

[8]  Charless C. Fowlkes,et al.  Multiresolution Models for Object Detection , 2010, ECCV.

[9]  Ari Juels,et al.  RFID security and privacy: a research survey , 2006, IEEE Journal on Selected Areas in Communications.

[10]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Bernt Schiele,et al.  A Performance Evaluation of Single and Multi-feature People Detection , 2008, DAGM-Symposium.

[12]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ioannis Brilakis,et al.  Construction worker detection in video frames for initializing vision trackers , 2012 .

[14]  Jonathan Brandt,et al.  Robust object detection via soft cascade , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

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

[17]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[18]  Patricio A. Vela,et al.  Personnel tracking on construction sites using video cameras , 2009, Adv. Eng. Informatics.

[19]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Tao Cheng,et al.  Modeling Tower Crane Operator Visibility to Minimize the Risk of Limited Situational Awareness , 2014 .

[22]  Jochen Teizer,et al.  Right-time vs real-time pro-active construction safety and health system architecture , 2016 .

[23]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[24]  Jochen Teizer,et al.  Heat map generation for predictive safety planning: preventing struck-by and near miss interactions between workers-on-foot and construction equipment , 2016 .

[25]  Tariq Shehab,et al.  Tracking Systems in Construction : Applications and Comparisons , 2013 .

[26]  Aviad Shapira,et al.  Identification and Analysis of Factors Affecting Safety on Construction Sites with Tower Cranes , 2009 .

[27]  Bernt Schiele,et al.  Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.

[28]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[30]  N S Seixas,et al.  A review of crane safety in the construction industry. , 2001, Applied occupational and environmental hygiene.