Visual Tracking of Construction Jobsite Workforce and Equipment with Particle Filtering

AbstractTracking workforce and equipment at construction jobsites has attracted considerable interest, considering its importance for productivity analysis, safety monitoring, and dynamic site layout planning, for example. Several real-time locating systems (RTLSs) are commercially available, but their requirements for attaching sensors or tags to a workers or equipment raise privacy concerns. Recently, the idea of using video cameras statically placed at construction jobsites to track workers and equipment has been proposed and tested. One challenge of visual tracking stems from jobsite occlusions, which significantly affect tracking performance. This paper presents a vision tracking method using particle filters to address the issue of occlusions at construction jobsites. The method includes two main phases. First, the worker or mobile equipment of interest is manually initiated with a rectangular window, and hundreds of particles are generated. Then each particle is propagated and its weight is calcula...

[1]  Jie Gong,et al.  An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations , 2011 .

[2]  Ioannis Brilakis,et al.  Comparative study of vision tracking methods for tracking of construction site resources , 2011 .

[3]  Xiaokang Yang,et al.  Camshift Guided Particle Filter for Visual Tracking , 2007, 2007 IEEE Workshop on Signal Processing Systems.

[4]  Sven J. Dickinson,et al.  Server-Customer Interaction Tracker: Computer Vision-Based System to Estimate Dirt-Loading Cycles , 2013 .

[5]  Chao Li,et al.  A robust approach for multiple vehicles tracking using layered particle filter , 2011 .

[6]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[7]  Suchendra M. Bhandarkar,et al.  Face detection and tracking using a Boosted Adaptive Particle Filter , 2009, J. Vis. Commun. Image Represent..

[8]  Justus H. Piater,et al.  Robust Non-rigid Object Tracking Using Point Distribution Manifolds , 2006, DAGM-Symposium.

[9]  Neil A. Thacker,et al.  Automatic Parameter Selection for Object Recognition Using a Parallel Multiobjective Genetic Algorithm , 1997, CAIP.

[10]  Zhongke Shi,et al.  Tracking multiple workers on construction sites using video cameras , 2010, Adv. Eng. Informatics.

[11]  Fakhreddine Ababsa Real-time camera tracking for structured environment using an iterated particle filter , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Cedric Nishan Canagarajah,et al.  Sequential Monte Carlo tracking by fusing multiple cues in video sequences , 2007, Image Vis. Comput..

[13]  Stephen J. McKenna,et al.  TRACKING POORLY MODELLED MOTION USING PARTICLE FILTERS WITH ITERATED LIKELIHOOD WEIGHTING , 2003 .

[14]  Mani Golparvar-Fard,et al.  Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors , 2013 .

[15]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[16]  Janaka Y. Ruwanpura,et al.  AUTOMATED DATA ACQUISITION SYSTEM TO ASSESS CONSTRUCTION WORKER PERFORMANCE , 2009 .

[17]  G.Mallikarjuna Rao,et al.  Visual Object Target Tracking Using Particle Filter: A Survey , 2013 .

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

[19]  Patrick Pérez,et al.  Variational inference for visual tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Andrzej Czyzewski,et al.  Performance evaluation of video object tracking algorithm in autonomous surveillance system , 2010, 2010 2nd International Conference on Information Technology, (2010 ICIT).

[21]  James J. Little,et al.  Tracking and recognizing actions of multiple hockey players using the boosted particle filter , 2009, Image Vis. Comput..

[22]  SangUk Han,et al.  A vision-based motion capture and recognition framework for behavior-based safety management , 2013 .

[23]  Tieniu Tan,et al.  Real-time hand tracking using a mean shift embedded particle filter , 2007, Pattern Recognit..

[24]  Alan Fern,et al.  Discriminatively trained particle filters for complex multi-object tracking , 2009, CVPR.

[25]  Hyoungkwan Kim,et al.  Using Hue, Saturation, and Value Color Space for Hydraulic Excavator Idle Time Analysis , 2007 .

[26]  Feniosky Peña-Mora,et al.  Vision-Based Detection of Unsafe Actions of a Construction Worker: Case Study of Ladder Climbing , 2013, J. Comput. Civ. Eng..

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

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

[29]  Fredrik Gustafsson,et al.  On Resampling Algorithms for Particle Filters , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

[30]  Patricio A. Vela,et al.  Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future , 2015, Adv. Eng. Informatics.

[31]  Brenda McCabe,et al.  Automated Visual Recognition of Dump Trucks in Construction Videos , 2012, J. Comput. Civ. Eng..

[32]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[33]  Mani Golparvar-Fard,et al.  Real-Time and Automated Recognition and 2D Tracking of Construction Workers and Equipment from Site Video Streams , 2012 .