Abstract
A common approach to pattern recognition and object detection is to use a statistical classifier. Widely used method is AdaBoost or its modifications which yields outstanding results in certain tasks like face detection. The aim of this work was to build real-time system for detection of dogs for surveillance purposes. The author of this paper thus explored the possibility that the AdaBoost based classifiers could be used for this task.
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References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features, pp. 511–518 (2001)
Šochman, J., Matas, J.: Learning a fast emulator of a binary decision process. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 236–245. Springer, Heidelberg (2007)
Sochman, J., Matas, J.: Waldboost learning for time constrained sequential detection. In: CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Washington, DC, USA, 2005, vol. 2, pp. 150–156. IEEE Computer Society Press, Los Alamitos (2005)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
Schapire, R.E., Singer, Y.: Machine learning. Improved boosting algorithms using confidence-rated predictions 37(3), 297–336 (1999)
Li, S., Zhang, Z., Shum, H., Zhang, H.: Floatboost learning for classification (2002)
Sochman, J., Matas, J.: Adaboost with totally corrective updates for fast face detection. In: FGR, pp. 445–450 (2004)
Wald, A.: Sequential Analysis. John Wiley and Sons, Inc., Chichester (1947)
Lee, T.S.: Image representation using 2d gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 959–971 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Zemčk, P., Hradiš, M., Herout, A.: Local rank differences - novel features for image. In: Proceedings of SCCG 2007, pp. 1–12 (2007).
Hradiš, M.: Framework for research on detection classifiers. In: Proceedings of Spring Conference on Computer Graphics, pp. 171–177 (2008)
Jones, M., Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: ICCV, pp. 734–741 (2003)
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Juránek, R. (2009). Detection of Dogs in Video Using Statistical Classifiers. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2008. Lecture Notes in Computer Science, vol 5337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02345-3_25
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DOI: https://doi.org/10.1007/978-3-642-02345-3_25
Publisher Name: Springer, Berlin, Heidelberg
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