Efficient Video Processing Method for Traffic Monitoring Combining Motion Detection and Background Subtraction

  • Roberts Kadiķis
  • Kārlis Freivalds
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 221)


A computationally efficient video processing algorithm for vehicle detection is presented. The algorithm detects vehicles that arrive on a defined detection line. Inter-frame difference is used to detect the presence of a vehicle and background subtraction is used to find the shape of the object that does not belong to an empty road. If movement is detected, intervals corresponding to the moving objects are created on the detection line. Further processing of intervals allows detecting and counting the objects. The accuracy of the proposed method is analyzed on different roads and in different weather conditions.


Vehicle detection Image processing Movement detection Background subtraction 



This work was supported by European Regional Development Fund project Nr.2010/0285/2DP/


  1. 1.
    Michalopoulos PG (1991) Vehicle detection video trough image processing: The autoscope system. IEEE Trans Veh Technol 40(1):21–29CrossRefGoogle Scholar
  2. 2.
    Beymer D, McLauchlan P, Coifman B, Malik J (1997) A real-time computer vision system for measuring traffic parameters. In: Proceeding of IEEE computer society conference on computer vision and pattern recognition. 495–501 Google Scholar
  3. 3.
    Kastrinaki V, Zervakis M, Kalaitzakis K (2003) A survey of video processing techniques for traffic applications. Image Vis Comput 21:359–381CrossRefGoogle Scholar
  4. 4.
    Rodriruez T, Garcia N (2010) An adaptive, real-time, traffic monitoring system. Mach Vis Appl 12:555–576CrossRefGoogle Scholar
  5. 5.
    Vibha L, Venkatesha M, Prasanth GR, Suhas N, Shenoy PD, Venugopal KR, Patnaik LM (2008) Moving vehicle identification using back-ground registration technique for traffic surveillance. In: Proceedings of the international multi conference of Engineers and Computer Scientists, vol 1Google Scholar
  6. 6.
    Ji X, Wei Z, Feng Y (2006) Effective vehicle detection technique for traffic surveillance systems. J Vis Commun Image Process 17:6477–6658Google Scholar
  7. 7.
    Gupte S, Masoud O, Martin RFK, Papanikolopoulus NP (2002) Detection and classification of vehicles. IEEE Trans Intell Transp Syst 3(1):37–47CrossRefGoogle Scholar
  8. 8.
    Hu Q, Li S, He K, Lin H (2010) A robust fusion method for vehicle detection in road traffic surveillance. In: Proceedings of the advanced intelligent computing theories and applications, and 6th international conference on intelligent computing. pp 180–187 Google Scholar
  9. 9.
    Cheung S-CS, Kamath C (2005) Robust background subtraction with fore-ground validation for Urban traffic video. EURASIP J Appl Signal Process 2005(1):2330–2340MATHCrossRefGoogle Scholar
  10. 10.
    Stauffer C, Eric W, Grimson L (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757CrossRefGoogle Scholar
  11. 11.
    Koller D, Weber J, Malik J (1994) Robust multiple car tracking with occlusion reasoning. Lect Notes Comput Sci 800:189–196CrossRefGoogle Scholar
  12. 12.
    Anan L, Zhaoxuan Y, Jintao L (2007) Video vehicle detection algorithm based on virtual-line group. In: Proceeding of IEEE APCCAS. pp 2854–2858 Google Scholar
  13. 13.
    Zhang G, Avery RP, Wang YA (2007) Video-based vehicle detection and classification system for real-time traffic data collection using uncalibrated video cameras. Transportation research record. J Transp Res Board 1993:138–147 Google Scholar
  14. 14.
    Lei M, Lefloch D, Gouton P, Madani K (2008) A video-based real-time vehicle counting system using adaptive background method. IEEE international conference on signal image technology and internet based systems. pp 523–528 Google Scholar
  15. 15.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Institute of Electronics and Computer ScienceRigaLatvia

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