Estimation of Traffic Occupancy using Image Segmentation


  • 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 |


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.


image segmentation, road occupancy, shadow removal


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M. Won, "Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey," IEEE Access, vol. 8, pp. 73340-73358, 2020.

N. K. Jain, R. K. Saini, and P. Mittal, "A Review on Traffic Monitoring System Techniques," in Soft Computing: Theories and Applications, Singapore, 2019, pp. 569-577.

M. Umer, N. Ahmed, and M. Ali, "Unsupervised Video Surveillance for Anomaly Detection of Street Traffic," International Journal of Advanced Computer Science and Applications, vol. 8, no. 12, pp. 270-275, Jan. 2017.

B. Tian et al., "Hierarchical and Networked Vehicle Surveillance in ITS: A Survey," IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 1, pp. 25-48, Jan. 2017.

A. Detho, S. R. Samo, K. C. Mukwana, K. A. Samo, and A. A. Siyal, "Evaluation of Road Traffic Accidents (RTAs) on Hyderabad Karachi M-9 Motorway Section," Engineering, Technology & Applied Science Research, vol. 8, no. 3, pp. 2875-2878, Jun. 2018.

S. Kamijo, Y. Matsushita, K. Ikeuchi, and M. Sakauchi, "Traffic monitoring and accident detection at intersections," in Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383), Tokyo, Japan, Oct. 1999, pp. 703-708.

C. C. C. Pang, W. W. L. Lam, and N. H. C. Yung, "A Method for Vehicle Count in the Presence of Multiple-Vehicle Occlusions in Traffic Images," IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 3, pp. 441-459, Sep. 2007.

E. Bas, A. M. Tekalp, and F. S. Salman, "Automatic Vehicle Counting from Video for Traffic Flow Analysis," in 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, Jun. 2007, pp. 392-397.

M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, "Saudi Arabian license plate recognition system," in 2003 International Conference on Geometric Modeling and Graphics, 2003. Proceedings, London, UK, Jul. 2003, pp. 36-41.

J. N. Saeed, "A Survey of Ultrasonography Breast Cancer Image Segmentation Techniques," Academic Journal of Nawroz University, vol. 9, no. 1, pp. 1-14, Feb. 2020.

W. S. Chowdhury, A. R. Khan, and J. Uddin, "Vehicle License Plate Detection Using Image Segmentation and Morphological Image Processing," in Advances in Signal Processing and Intelligent Recognition Systems, Manipal, India, 2018, pp. 142-154.

S. Bargoti and J. P. Underwood, "Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards," Journal of Field Robotics, vol. 34, no. 6, pp. 1039-1060, 2017.

K. K. Santhosh, D. P. Dogra, and P. P. Roy, "Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey," ACM Computing Surveys, vol. 53, no. 6, p. 119:1-119:26, Dec. 2020.

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, "Segmentation of images by color features: A survey," Neurocomputing, vol. 292, pp. 1-27, May 2018.


How to Cite

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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