Measurement of Road Traffic Parameters based on Multi-Vehicle Tracking

Abstract

Development of computing power and cheap video cameras enabled today’s traffic management systems to include more cameras and computer vision based applications for monitoring and control of road transportation systems. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow volume, origin-destination matrices, classify vehicles, etc. In this paper we propose a system for measurement of road traffic parameters (basic motion model parameters and macro-scopic traffic parameters). The system is based on Local Binary Pattern image features classification with a cascade of Gentle Adaboost classifiers to determine vehicle existence and its location in an image. Additionally, vehicle tracking and counting in a road traffic video is performed by using Extended Kalman Filter and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by means of evaluating execution time and accuracy.

Files

Full Paper as PDF

BibTeX Citation

DOI

10.20532/ccvw.2015.0002

https://doi.org/10.20532/ccvw.2015.0002

BibTeX

@InProceedings{10.20532/ccvw.2015.0002,
  author =       {Kristian Kova{\v c}i{\' c} and Edouard Ivanjko and
                  Niko Jelu{\v s}i{\' c}},
  title =        {Measurement of Road Traffic Parameters based on
                  Multi-Vehicle Tracking},
  booktitle =    {Proceedings of the Croatian Compter Vision Workshop,
                  Year 3},
  pages =        {3-8},
  year =         2015,
  editor =       {Lon{\v c}ari{\' c}, Sven and Krapac, Josip},
  address =      {Zagreb},
  month =        {September},
  organization = {Center of Excellence for Computer Vision},
  publisher =    {University of Zagreb},
  abstract =     {Development of computing power and cheap video
                  cameras enabled today's traffic management systems
                  to include more cameras and computer vision
                  applications for transportation system monitoring
                  and control. Combined with image processing
                  algorithms cameras are used as sensors to measure
                  road traffic parameters like flow volume,
                  origin-destination matrices, classify vehicles,
                  etc. In this paper we propose a system for
                  measurement of road traffic parameters (basic motion
                  model parameters and macro-scopic traffic
                  parameters). The system is based on Local Binary
                  Pattern (LBP) image features classification with a
                  cascade of Gentle Adaboost (GAB) classifiers to
                  determine vehicle existence and its location in an
                  image. Additionally, vehicle tracking and counting
                  in a road traffic video is performed by using
                  Extended Kalman Filter (EKF) and virtual
                  markers. The newly proposed system is compared with
                  a system based on background subtraction. Comparison
                  is performed by the means of execution time and
                  accuracy.},
  doi =          {10.20532/ccvw.2015.0002},
  url =          {https://doi.org/10.20532/ccvw.2015.0002}
}