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Speed Estimation and Detection of Moving Vehicles Based on Probabilistic Principal Component Analysis and New Digital Image Processing Approach

  • T. V. Mini
  • V. Vijayakumar
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

In the twenty-first century, smart city surveillance management is one of the advancements of Information and Communication Technology. Intelligent Transport System (ITS) is an essential component of the smart city. Moving vehicle detection and speed estimation are major tasks of traffic management. Vehicle tracking and speed measurement methods failed to achieve good accuracy rate due to unsuccessful detection of moving vehicles. In the existing system, the conventional de-noising filters reduce the noise in smooth regions. The edges of object boundaries are not sharply identified. In this chapter, the Probabilistic Principal Component Analysis (PPCA) method is proposed to detect multiple outliers in objects. It is computationally fast and robust in identifying outliers which helps to reduce the dimension of video by finding an alternate set of coordinates. The proposed approach consists of three stages. First, Spatio-temporal Varying Filter (STVF) is applied to preprocess extracted frames. Contour finding algorithm is used to detect the vehicle. The frame count scheme is applied to estimate the vehicle speed. This approach provides high detection accuracy with high precision and recall rate in BrnoCompSpeed dataset.

Keywords

Smart city Speed estimation and detection (SED) Digital image processing 

References

  1. 1.
    B.C. Putra, Moving vehicle classification with fuzzy logic based on image processing, Master’s thesis, Sepuluh Nopember Institute of Technology (2016)Google Scholar
  2. 2.
    V. Markevicius, D. Navikas, A. Idzkowski, A. Valinevicius, M. Zilys, D. Andriukaitis, Vehicle speed and length estimation using data from two anisotropic magneto-resistive (AMR) sensors. Sensors 17(8), 1–13 (2017)CrossRefGoogle Scholar
  3. 3.
    D.F. Llorca, C. Salinas, M. Jimenez, I. Parra, A.G. Morcillo, R. Izquierdo, J. Lorenzo, M.A. Sotelo, Two-camera based accurate vehicle speed measurement using average speed at a fixed point, in IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (2016), pp. 2533––2538Google Scholar
  4. 4.
    F. Al-Turjman, Vehicular speed learning in the future smart-cities' paradigm, in IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops) (2017), pp. 61–65.Google Scholar
  5. 5.
    H. Wang, W. Quan, X. Liu, S. Zhang, A two seismic sensor based approach for moving vehicle detection. Procedia-Social Behav. Sci. 96, 2647–2653 (2013)CrossRefGoogle Scholar
  6. 6.
    G. Guido, V. Gallelli, D. Rogano, A. Vitale, Evaluating the accuracy of vehicle tracking data obtained from unmanned aerial vehicles. Int. J. Transp. Sci. Technol. 5(3), 136–151 (2016)CrossRefGoogle Scholar
  7. 7.
    T. Kumar, D.S. Kushwaha, An efficient approach for detection and speed estimation of moving vehicles. Procedia Comput. Sci. 89, 726–731 (2016)CrossRefGoogle Scholar
  8. 8.
    Q. Wei, B. Yang, Adaptable vehicle detection and speed estimation for changeable urban traffic with anisotropic magnetoresistive sensors. IEEE Sens. J. 17(7), 2021–2028 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    W. Wu, V. Kozitsky, M.E. Hoover, R. Loce, D.T. Jackson, Vehicle speed estimation using a monocular camera, in Video Surveillance and Transportation Imaging Applications. International Society for Optics and Photonics (2015), Vol. 9407, pp. 704–940Google Scholar
  10. 10.
    S. Rajab, M.O. Al Kalaa, H. Refai, Classification and speed estimation of vehicles via tire detection using single-element piezoelectric sensor. J. Adv. Transp. 50(7), 1366–1385 (2016)CrossRefGoogle Scholar
  11. 11.
    Y. Li, L. Yin, Y. Jia, M. Wang, Vehicle speed measurement based on video images, in 3rd International Conference on Innovative Computing Information and Control (2008), pp. 439–439Google Scholar
  12. 12.
    J. Lan, J. Li, G. Hu, B. Ran, L. Wang, Vehicle speed measurement based on gray constraint optical flow algorithm. Optik-Int. J. Light Electron Optics 125(1), 289–295 (2014)CrossRefGoogle Scholar
  13. 13.
    D. Jeyabharathi, D. Dejey, Vehicle Tracking and Speed Measurement system (VTSM) based on novel feature descriptor: Diagonal Hexadecimal Pattern (DHP). J. Vis. Commun. Image Represent. 40, 816–830 (2016)CrossRefGoogle Scholar
  14. 14.
    H. Abdi, L.J. Williams, Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010)CrossRefGoogle Scholar
  15. 15.
    R. Bro, A.K. Smilde, Principal component analysis. Anal. Methods 6(9), 2812–2831 (2014)CrossRefGoogle Scholar
  16. 16.
    M.E. Tipping, C.M. Bishop, Probabilistic principal component analysis. J. Roy. Stat. Soc. B 61(3), 611–622 (1999)MathSciNetCrossRefGoogle Scholar
  17. 17.
    J. Seo, S. Chae, J. Shim, D. Kim, C. Cheong, T.D. Han, Fast contour-tracing algorithm based on a pixel-following method for image sensors. Sensors 16(3), 353 (2016)CrossRefGoogle Scholar
  18. 18.
    A.B. Hamida, M. Koubaa, H. Nicolas, C.B. Amar, Spatio-temporal video filtering for video surveillance applications, in IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (2013), pp. 1–6Google Scholar
  19. 19.
    A.B. Hamida, M. Koubaa, H. Nicolas, C.B. Amar, Spatio-temporal video filtering for video surveillance applications, in IEEE International Conference on Multimedia and Expo Workshops (ICMEW), (2013), pp. 1–6Google Scholar
  20. 20.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • T. V. Mini
    • 1
  • V. Vijayakumar
    • 2
  1. 1.Sacred Heart CollegeChalakudyIndia
  2. 2.Sri Ramakrishna College of Arts and ScienceCoimbatoreIndia

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