Public And Private Service Vehicle Classification In Bogotá using SVM and AdaBoost

Authors

  • Francisco Calderon Pontificia Universidad Javeriana
  • Carlos Alberto Parra Pontificia Universidad Javeriana

Keywords:

AdaBoost, Binary Trees, OpenCV, Pattern recognition, SVM, Traffic engineering, Vehicles classification

Abstract

This paper presents the implementation and comparison of algorithms for support vector machines “SVM” and AdaBoost in the classification of public and private vehicles using segmented images of video sequences taken in Bogotá city. Using as tools the OpenCV libraries implemented in C. The algorithms performances are remarkable and therefore its use could have a positive impact in the reduction of traffic problems.

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References

G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Inc., 2008.

C. J. Burges, “A tutorial on support vector machines for pattern recogni- tion,” ell Laboratories, Lucent Technologies, 1998.

Calderon.F and Urrego.G, Conteo Automático De Vehículos. PUJ, Nov. 2008.

C. Cordoba, “Sobre la reproducción de la placa nacional,” Transito Y Transporte resolucion 538, 2001.

R. Duda, P. Hart, and D. Stork, Pattern classification. Wiley New York, 2001.

C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A practical guide to support vector classification,” Department of Computer Science, Oct. 2008.

Yoav.F and Schapire. R.E, “A short introduction to boosting,” Japanese Society for Artificial Intelligence, Sep. 1999.

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Published

2013-09-09
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How to Cite

Calderon, F., & Parra, C. A. (2013). Public And Private Service Vehicle Classification In Bogotá using SVM and AdaBoost. INGE@UAN - TENDENCIAS EN LA INGENIERÍA, 3(5). Retrieved from https://revistas.uan.edu.co/index.php/ingeuan/article/view/348

Issue

Vol. 3 No. 5 (2012)

Section

Artículo de investigación científica y tecnológica

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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