Estimation of Traffic Occupancy using Image Segmentation

Authors

  • M. U. Farooq Department of Computer Science & Information Technology, NED University of Engineer and Technology, Pakistan
  • A. Ahmed Department of Urban and Infrastructure Engineering, NED University of Engineering and Technology, Pakistan
  • S. M. Khan Department of Computer Science and IT, NED University of Engineering and Technology, Pakistan
  • M. B. Nawaz Department of Urban and Infrastructure Engineering, NED University of Engineering and Technology, Pakistan
Volume: 11 | Issue: 4 | Pages: 7291-7295 | August 2021 | https://doi.org/10.48084/etasr.4218

Abstract

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.

Keywords:

image segmentation, road occupancy, shadow removal

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How to Cite

[1]
M. U. Farooq, A. Ahmed, S. M. Khan, and M. B. Nawaz, “Estimation of Traffic Occupancy using Image Segmentation”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 4, pp. 7291–7295, Aug. 2021.

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