Applying Image Features and AdaBoost Classification for Vehicle Detection in the ‘SM4Public’ System

  • Dariusz FrejlichowskiEmail author
  • Katarzyna Gościewska
  • Paweł Forczmański
  • Adam Nowosielski
  • Radosław Hofman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 389)


The main goal of works described in the paper is to test and select algorithms to be implemented in the ‘SM4Public’ security system for public spaces. The paper describes the use of cascading approaches in the scenario concerning the detection of vehicles in static images. Three feature extractors were used along with benchmark datasets in order to prepare eight various cascades of classifiers. The algorithms selected for feature extraction are Histogram of Oriented Gradients, Local Binary Patterns and Haar-like features. AdaBoost was used as a classifier. The paper briefly introduces the ‘SM4Public’ system characteristics, characterizes the employed algorithms and presents sample experimental results.


Video surveillance Vehicle detection Cascades of classifiers 



The project “Security system for public spaces—‘SM4Public’ prototype construction and implementation” (original title: Budowa i wdrożenie prototypu systemu bezpieczeństwa przestrzeni publicznej ‘SM4Public’) is a project co-founded by European Union (EU) (project number PL: POIG.01.04.00-32-244/13, value: 12.936.684,77 PLN, EU contribution: 6.528.823,81 PLN, realization period: 01.06.2014–31.10.2015). European Funds-for the development of innovative economy (Fundusze Europejskie-dla rozwoju innowacyjnej gospodarki).


  1. 1.
    Aarthi, R., Padmavathi, S., Amudha, J.: Vehicle detection in static images using color and corner map. In: 2010 International Conference on Recent Trends in Information, Telecommunication and Computing, pp. 244–246 (2010)Google Scholar
  2. 2.
    Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern Anal. 26(11), 1475–1490 (2004)CrossRefGoogle Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 886–893 (2005)Google Scholar
  4. 4.
    Forczmański, P., Seweryn, M.: Surveillance video stream analysis using adaptive background model and object recognition. In: Bolc, L. et al. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 114–121. Springer, Berlin (2010)Google Scholar
  5. 5.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Hofman, R.: Application of foreground object patterns analysis for event detection in an innovative video surveillance system. Pattern Anal. Appl. 1–12 (2014)Google Scholar
  6. 6.
    Frejlichowski, D., Gościewska, K., Forczmański, P., Hofman, R.: ‘SmartMonitor’—an intelligent security system for the protection of individuals and small properties with the possibility of home automation. Sensors 14, 9922–9948 (2014)CrossRefGoogle Scholar
  7. 7.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European Conference on Computational Learning Theory, pp. 23–37 (1995)Google Scholar
  8. 8.
    Google Project: OpenCV implementation of lane and vehicle tracking.
  9. 9.
    He, D.C., Wang, L.: Texture unit, texture spectrum, and texture analysis. IEEE Trans. Geosci. Remote 28, 509–512 (1990)CrossRefGoogle Scholar
  10. 10.
    Ojala, T., Pietikinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th International Conference on Pattern Recognition, vol. 1, pp. 582–585 (1994)Google Scholar
  11. 11.
    Tang, Y., Zhang, C., Gu, R., Li, P., Yang, B.: Vehicle detection and recognition for intelligent traffic surveillance system. In: Multimedia Tools and Applications (online). Springer (2015)Google Scholar
  12. 12.
    Trefný, J., Matas, J.: Extended set of local binary patterns for rapid object detection. In: Špaček, L., Franc, V. (eds.) Computer Vision Winter Workshop 2010, Nové Hrady, Czech Republic, Czech Society for Cybernetics and Informatics (2010)Google Scholar
  13. 13.
    Tsai, L.-W., Hsieh, J.-W., Fan, K.-C.: Vehicle detection using normalized color and edge map. IEEE Trans. Image Process. 16(3), 850–864 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Wu, C., Duan, L., Miao, J., Fang, F., Wang, X.: Detection of front-view vehicle with occlusions using AdaBoost. In: International Conference on Information Engineering and Computer Science, pp. 1–4, Wuhan (2009)Google Scholar
  16. 16.
    Zhu, Q., Avidan, S., Yeh, M.-C., Cheng, K.-T.: Fast human detection using a cascade of histograms of oriented gradients. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 1491–1498 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dariusz Frejlichowski
    • 1
    Email author
  • Katarzyna Gościewska
    • 1
    • 2
  • Paweł Forczmański
    • 1
  • Adam Nowosielski
    • 1
  • Radosław Hofman
    • 2
  1. 1.Faculty of Computer ScienceWest Pomeranian University of Technology, SzczecinSzczecinPoland
  2. 2.Smart Monitor sp. z o.o.SzczecinPoland

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