False Positive Reduction in Detector Implantation

  • Noelia Vállez
  • Gloria Bueno
  • Oscar Déniz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)

Abstract

The development of a detection system is normally driven to achieve good detection rates. In most cases, a good detection rate involves a number of false positive decisions. However, the false positive rate is ultimately what decides if the detection system is effective or not. Another aspect to consider in automatic detection systems is the time to analyse an image until a decision is made. Viola & Jones proposed a cascade detector that achieves good detection and false positive rates at high speed. Some authors have proposed modifications to the cascade detector in order to improve the detection rate while maintaining the same false positive rate. However, during the implantation of the system we consistently find a large number of false positive detections due to the lack of knowledge about the newly acquired images. In this work, we propose a parallel cascade detector that gradually incorporates these new false positives to achieve an acceptable false positive rate. The second cascade detector is built using the new false positive detection images and the original true positive images during the implantation period. The proposed parallel scheme reduces the false positive rate of the system at roughly the same speed.

Keywords

False Positive Reduction Automatic Detection Cascade of Classifiers 

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References

  1. 1.
    Viola, P.A., Jones, M.J.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: CVPR (1), pp. 511–518 (2001)Google Scholar
  2. 2.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection, vol. 1, pp. 900–903 (2002)Google Scholar
  3. 3.
    Sochman, J., Matas, J.: Inter-stage feature propagation in cascade building with adaboost. In: ICPR (1), pp. 236–239Google Scholar
  4. 4.
    Chen, Y.-T., Chen, C.-S.: A cascade of feed-forward classifiers for fast pedestrian detection. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 905–914. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Cheng, W.C., Jhan, D.M.: A cascade classifier using Adaboost algorithm and support vector machine for pedestrian detection. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1430–1435 (2011)Google Scholar
  6. 6.
    Kuncheva, L.I.: Combining Pattern Classifiers Methods and Algorithms. John Wiley & Sons, Inc. (2004)Google Scholar
  7. 7.
    Castrillón, M., Déniz, O., Hernández, D., Lorenzo, J.: A comparison of face and facial feature detectors based on the Viola & Jones general object detection framework. Machine Vision and Applications 22, 481–494 (2011)Google Scholar
  8. 8.
    Kuncheva, L.I., Whitaker, C.J., Shipp, C.A.: Limits on the Majority Vote Accuracy in Classifier Fusion. Pattern Analysis and Applications 6, 22–31 (2003)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Agresti, A.: An introduction to categorical data analysis. John Wiley & Sons, Inc. (1996)Google Scholar
  10. 10.
    Yule, G.: On the association of attributes in statistics. Phil. Trans. 194, 257–319 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Noelia Vállez
    • 1
  • Gloria Bueno
    • 1
  • Oscar Déniz
    • 1
  1. 1.VISILAB Group, ETSI IndustrialesUniversity of Castilla - La Mancha (UCLM)Ciudad RealSpain

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