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

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 221)

Abstract

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.

Keywords

  • Vehicle detection
  • Image processing
  • Movement detection
  • Background subtraction

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  • DOI: 10.1007/978-81-322-0997-3_12
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References

  1. Michalopoulos PG (1991) Vehicle detection video trough image processing: The autoscope system. IEEE Trans Veh Technol 40(1):21–29

    CrossRef  Google Scholar 

  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. Kastrinaki V, Zervakis M, Kalaitzakis K (2003) A survey of video processing techniques for traffic applications. Image Vis Comput 21:359–381

    CrossRef  Google Scholar 

  4. Rodriruez T, Garcia N (2010) An adaptive, real-time, traffic monitoring system. Mach Vis Appl 12:555–576

    CrossRef  Google Scholar 

  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 1

    Google Scholar 

  6. Ji X, Wei Z, Feng Y (2006) Effective vehicle detection technique for traffic surveillance systems. J Vis Commun Image Process 17:6477–6658

    Google Scholar 

  7. Gupte S, Masoud O, Martin RFK, Papanikolopoulus NP (2002) Detection and classification of vehicles. IEEE Trans Intell Transp Syst 3(1):37–47

    CrossRef  Google Scholar 

  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. Cheung S-CS, Kamath C (2005) Robust background subtraction with fore-ground validation for Urban traffic video. EURASIP J Appl Signal Process 2005(1):2330–2340

    MATH  CrossRef  Google Scholar 

  10. Stauffer C, Eric W, Grimson L (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757

    CrossRef  Google Scholar 

  11. Koller D, Weber J, Malik J (1994) Robust multiple car tracking with occlusion reasoning. Lect Notes Comput Sci 800:189–196

    CrossRef  Google Scholar 

  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. 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. 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. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    MathSciNet  CrossRef  Google Scholar 

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Acknowledgments

This work was supported by European Regional Development Fund project Nr.2010/0285/2DP/2.1.1.1.0/10/APIA/VIAA/086

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Correspondence to Roberts Kadiķis .

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Kadiķis, R., Freivalds, K. (2013). Efficient Video Processing Method for Traffic Monitoring Combining Motion Detection and Background Subtraction. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 221. Springer, India. https://doi.org/10.1007/978-81-322-0997-3_12

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  • DOI: https://doi.org/10.1007/978-81-322-0997-3_12

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-0996-6

  • Online ISBN: 978-81-322-0997-3

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