Efficient Detection and Counting of Moving Vehicles with Region-Level Analysis of Video Frames

  • Saroj K. Meher
  • M. N. Murty
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 131)


The present article discusses the problem of detecting and counting of moving vehicle (MV) in a road traffic scenario, where background subtraction (BS) plays a vital role. BS in a video sequence is an open problem with many practical applications including camera surveillance system, human-computer interactions, etc. Among the various methods of BS, frame difference method is a simple and most adopted one. However, the performance of frame deference method depends on the proper selection of a set of frames. To meet this problem, we describe an efficient and fast processing approach for detecting and counting of MVs. A region/block-level analysis of frames is performed in this approach which requires less processing time and provides more accurate results compared to the conventional pixel-level analysis. We have used fuzzy flood fill mean shift based segmentation algorithm for this present study, which is robust under the illumination effects; such as shadows, shades, and highlights. In pixel-level analysis, segmentation operation is performed on the difference frame obtained from two test frames and detection of MV is made subsequently. However, detection with region-level analysis is made based on the geometric movement of regions between frames. Performance comparison between these two methods is made and superiority of the region-level based analysis is validated through examples. Also, we describe the estimation of the number of vehicles in a multiple MV traffic scenarios.


Intelligent transport system vehicle tracking moving objects segmentation vehicles detection fuzzy logic 


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  1. 1.
    Ki, Y.-K., Lee, D.-Y.: A traffic accident recording and reporting model at intersections. IEEE Trans. Intel. Transport Syst. 8, 188–194 (2007)CrossRefGoogle Scholar
  2. 2.
    Chen, S.-C., Shyu, M.-L., Peeta, S., Zhang, C.: Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking. IEEE Magazine, Robotics and Automation 12, 50–58 (2005)CrossRefGoogle Scholar
  3. 3.
    Zhang, W., Fang, X.Z., Yang, X.: Moving vehicles segmentation based on bayesian framework for gaussian motion model. Pattern Recognit. Lett. 27, 956–967 (2006)CrossRefGoogle Scholar
  4. 4.
    Kanhere, N.K., Birchfield, S.T.: Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features. IEEE Trans. Intel. Transport Syst. 9, 148–160 (2008)CrossRefGoogle Scholar
  5. 5.
    Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21, 32–40 (1975)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Machine Intel. 17, 790–799 (1995)CrossRefGoogle Scholar
  7. 7.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intel. 24, 603–619 (2002)CrossRefGoogle Scholar
  8. 8.
    Kang, H., Lee, S.H., Lee, J.: Image segmentation based on fuzzy flood fill mean shift algorithm. In: 2010 Annual Meeting of the North American in IEEE Fuzzy Information Processing Society (2010)Google Scholar

Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  1. 1.Systems Science and Informatics UnitIndian Statistical InstituteBangaloreIndia
  2. 2.Dept. of CSAIndian Institute of ScienceBangaloreIndia

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