Framework for Illumination Invariant Vehicular Traffic Density Estimation

  • Pranam Janney
  • Glenn Geers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


CCTV cameras are becoming a common fixture at the roadside. Their use varies from traffic monitoring to security surveillance. In this paper a novel technique, using Invariant Features of Local Textures (IFLT) & Support Vector Machine (SVM), for estimating vehicular traffic density on a road segment is presented. The proposed approach is computationally efficient and robust to varying illumination. Experimental results have shown that the proposed framework can achieve high performance than extant state-of-the-art techniques in varying illumination conditions.


Intelligent Transport Systems (ITS) Invariant Features of Local Textures (IFLT) Support Vector Machines (SVM) density estimation traffic information parameters illumination invariance 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pranam Janney
    • 1
  • Glenn Geers
    • 1
  1. 1.Dept of Computer Science and EngineeringUniversity of New South Wales, Australia and, National ICT Australia (NICTA)SydneyAustralia

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