Dynamic blindspots measurement for construction equipment operators

Limited visibility due to blindspots of construction equipment is responsible for more than half of the visibility-related fatalities in the construction industry. Knowledge of blindspots aids in improving safety on construction sites and the design of the equipment cabin itself. We present a novel technique that allows measuring blindspots dynamically using the head posture of the equipment operator. To achieve this, we perform the head posture estimation with a range camera using Random Forests algorithm. Utilizing the head posture information and the point cloud data of the construction equipment, we generate dynamic blindspots or visibility maps from the operators perspective in real-time. Research findings suggest this approach can potentially be used as an intelligent alert system. Language: en

[1]  Roger V. Bostelman,et al.  Methods for improving visibility measurement standards of powered industrial vehicles , 2014 .

[2]  Harald Wuest,et al.  Linear-projection-based classification of human postures in time-of-flight data , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

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

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

[5]  Luc Van Gool,et al.  An Introduction to Random Forests for Multi-class Object Detection , 2011, Theoretical Foundations of Computer Vision.

[6]  Amin Hammad,et al.  Dynamic equipment workspace generation for improving earthwork safety using real-time location system , 2015, Adv. Eng. Informatics.

[7]  Carlos H. Caldas,et al.  Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources , 2007 .

[8]  Jochen Teizer,et al.  Automating the blind spot measurement of construction equipment , 2010 .

[9]  David G. Stork,et al.  Pattern Classification , 1973 .

[10]  Luc Van Gool,et al.  Real time head pose estimation with random regression forests , 2011, CVPR 2011.

[11]  Zhongke Shi,et al.  A performance evaluation of vision and radio frequency tracking methods for interacting workforce , 2011, Adv. Eng. Informatics.

[12]  Carlos H. Caldas,et al.  Framework for Real-Time Three-Dimensional Modeling of Infrastructure , 2005 .

[13]  Eric Marks,et al.  Laser Scanning for Safe Equipment Design That Increases Operator Visibility by Measuring Blind Spots , 2013 .

[14]  Patricio A. Vela,et al.  Performance evaluation of ultra wideband technology for construction resource location tracking in harsh environments , 2011 .

[15]  Todd M Ruff,et al.  Preventing collisions involving surface mining equipment: a GPS-based approach. , 2003, Journal of safety research.

[16]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[17]  Zifeng Wu,et al.  Real-Time Anticollision System for Mobile Cranes during Lift Operations , 2015 .

[18]  Tao Cheng,et al.  Proximity hazard indicator for workers-on-foot near miss interactions with construction equipment and geo-referenced hazard areas , 2015 .

[19]  Todd Ruff,et al.  Evaluation of a radar-based proximity warning system for off-highway dump trucks. , 2006, Accident; analysis and prevention.

[20]  Jochen Teizer.  Safety 360: Surround-View Sensing to Comply with Changesto the ISO 5006 Earth-Moving Machinery - Operator's Fieldof View - Test Method and Performance Criteria , 2015 .

[21]  Jochen Teizer,et al.  Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites , 2015, Adv. Eng. Informatics.

[22]  Vincent Lepetit,et al.  Randomized trees for real-time keypoint recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Jochen Teizer.  Wearable, wireless identification sensing platform: Self-Monitoring Alert and Reporting Technology for Hazard Avoidance and Training (SmartHat) , 2015, J. Inf. Technol. Constr..

[24]  Min Sun,et al.  Conditional regression forests for human pose estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Luc Van Gool,et al.  Random Forests for Real Time 3D Face Analysis , 2012, International Journal of Computer Vision.

[26]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Jochen Teizer,et al.  Automatic spatio-temporal analysis of construction site equipment operations using GPS data , 2013 .

[28]  Jochen Teizer,et al.  Automated Collection, Identification, Localization, and Analysis of Worker-Related Proximity Hazard Events in Heavy Construction Equipment Operation , 2015 .

[29]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[30]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[31]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Jochen Teizer,et al.  Computing 3D blind spots of construction equipment: Implementation and evaluation of an automated measurement and visualization method utilizing range point cloud data , 2013 .

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

[34]  Luc Van Gool,et al.  Real Time Head Pose Estimation from Consumer Depth Cameras , 2011, DAGM-Symposium.

[35]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Amin Hammad,et al.  Risk-based look-ahead workspace generation for earthwork equipment using near real-time simulation , 2015 .

[37]  Roger V. Bostelman,et al.  Improved Methods for Evaluation of Visibility for Industrial Vehicles Towards Safety Standards , 2013 .

[38]  Antonio Criminisi,et al.  Regression Forests for Efficient Anatomy Detection and Localization in CT Studies , 2010, MCV.

[39]  Arthur Appel,et al.  Some techniques for shading machine renderings of solids , 1968, AFIPS Spring Joint Computing Conference.

[40]  Moshe Eizenman,et al.  General theory of remote gaze estimation using the pupil center and corneal reflections , 2006, IEEE Transactions on Biomedical Engineering.

[41]  Jochen Teizer,et al.  Congestion Analysis for Construction Site Layout Planning using Real-Time Data and Cell-Based Simulation Model , 2014 .

[42]  Carlos H. Caldas,et al.  Evaluation of sensing technology for the prevention of backover accidents in construction work zones , 2014, J. Inf. Technol. Constr..

[43]  Jochen Teizer,et al.  Cell-based construction site simulation model for earthmoving operations using real-time equipment location data , 2015 .

[44]  Jochen Teizer,et al.  Coarse head pose estimation of construction equipment operators to formulate dynamic blind spots , 2012, Adv. Eng. Informatics.