Framework for Illumination Invariant Vehicular Traffic Density Estimation

Authors:
Pranam Janney;Glenn Geers
Affiliations:
Dept of Computer Science and Engineering, University of New South Wales, Australia and, National ICT Australia (NICTA), Sydney, Australia;Dept of Computer Science and Engineering, University of New South Wales, Australia and, National ICT Australia (NICTA), Sydney, Australia
Venue:
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Year:
2009

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Abstract

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.