Pre-attention Cues for Person Detection

  • Karel Paleček
  • David Gerónimo
  • Frédéric Lerasle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7403)


Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.


person detection candidate window generation pre-attention 


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  1. 1.
    Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 73–80 (2010)Google Scholar
  2. 2.
    Bertozzi, M., Broggi, A., Del Rose, M., Felisa, M.: A symmetry-based validator and refinement system for pedestrian detection in far infrared images. In: Intelligent Transportation Systems Conference, pp. 155–160. IEEE (2007)Google Scholar
  3. 3.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)CrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), software available at
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995), doi:10.1007/BF00994018zbMATHGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893. IEEE Computer Society (2005)Google Scholar
  7. 7.
    Enzweiler, M., Gavrila, D.M.: Monocular Pedestrian Detection: Survey and Experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2179–2195 (2009)CrossRefGoogle Scholar
  8. 8.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1627–1645 (2010)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  10. 10.
    Fong, T., Nourbakhsh, I., Dautenhahn, K.: A survey of socially interactive robots (2003)Google Scholar
  11. 11.
    Gerónimo, D., López, A.M., Sappa, A.D., Graf, T.: Survey of Pedestrian Detection for Advanced Driver Assistance Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(7), 1239–1258 (2010)CrossRefGoogle Scholar
  12. 12.
    Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (June 2007)Google Scholar
  13. 13.
    Levi, K., Weiss, Y.: Learning object detection from a small number of examples: the importance of good features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004), pp. 53–60. IEEE Computer Society (2004)Google Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Ogale, N.A.: A survey of techniques for human detection from video. Survey, University of Maryland (2006)Google Scholar
  16. 16.
    Schauland, S., Kummert, A., Park, S.B., Iurgel, U., Zhang, Y.: Vision-based pedestrian detection – improvement and verification of feature extraction methods and svm-based classification. In: Intelligent Transportation Systems Conference, ITSC 2006, pp. 97–102. IEEE (September 2006)Google Scholar
  17. 17.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. 511–518 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Karel Paleček
    • 1
  • David Gerónimo
    • 2
  • Frédéric Lerasle
    • 3
    • 4
  1. 1.Institute of Information Technology and ElectronicsTechnical University of LiberecCzech Republic
  2. 2.Computer Vision CenterAutonomous University of BarcelonaSpain
  3. 3.CNRS: LAASToulouseFrance
  4. 4.Université de Toulouse, UPS, INSA, INP, ISAE, LAAS-CNRSToulouseFrance

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