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Computer Vision Approaches to Pedestrian Detection: Visible Spectrum Survey

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Pattern Recognition and Image Analysis (IbPRIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4477))

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Abstract

Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study.

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References

  1. Gavrila, D., Giebel, J., Munder, S.: Vision–based pedestrian detection: The PROTECTOR system. In: IV, Parma, Italy (2004)

    Google Scholar 

  2. Zhao, L., Thorpe, C.: Stereo and neural network–based pedestrian detection. TITS 1(3), 148–154 (2000)

    Google Scholar 

  3. Broggi, A., Fascioli, A., Fedriga, I., Tibaldi, A., Del Rose, M.: Stereo–based preprocessing for human shape localization in unstructured environments. In: IV, Columbus, OH, USA, pp. 410–415 (2003)

    Google Scholar 

  4. Labayrade, R., Aubert, D., Tarel, J.: Real time obstacle detection in stereovision on non flat road geometry through v–disparity representation. In: IV, Versailles, France (2002)

    Google Scholar 

  5. Broggi, A., Bertozzi, M., Fascioli, A., Sechi, M.: Shape–based pedestrian detection. In: IV, Dearborn, MI, USA (2000)

    Google Scholar 

  6. Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: single–frame classification and system level performance. In: IV, Parma, Italy (2004)

    Google Scholar 

  7. Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  8. Munder, S., Gavrila, D.: An experimental study on pedestrian classification. TPAMI 21(11), 1863–1868 (2006)

    Google Scholar 

  9. Franke, U., Gavrila, D.: Autonomous driving goes downtown. IS 13(6), 40–48 (1999)

    Google Scholar 

  10. Bertozzi, M., Broggi, A., Fascioli, A., Tibaldi, A., Chapuis, R., Chausse, A.: Pedestrian localization and tracking system with Kalman filtering. In: IV, Parma, Italy, pp. 584–589 (2004)

    Google Scholar 

  11. Grubb, G., Zelinsky, A., Nilsson, L., Rilbe, M.: 3D vision sensing for improved pedestrian safety. In: IV, Parma, Italy (2004)

    Google Scholar 

  12. Gerónimo, D., Sappa, A., López, A., Ponsa, D.: Pedestrian detection using AdaBoost learning of features and vehicle pitch estimation. In: Proc. of the International Conference on Visualization, Imaging and Image Processing, Palma de Mallorca, Spain, pp. 400–405 (2006)

    Google Scholar 

  13. Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38(1), 15–33 (2000)

    Article  MATH  Google Scholar 

  14. Mohan, A., Papageorgiou, C., Poggio, T.: Example–based object detection in images by components. TPAMI 23(4), 349–361 (2001)

    Google Scholar 

  15. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, San Diego, CA, USA, vol. 2, pp. 886–893 (2005)

    Google Scholar 

  16. van der Mark, W., Gavrila, D.: Real–time dense stereo for intelligent vehicles. TITS 7(1), 38–50 (2006)

    Google Scholar 

  17. Gavrila, D.: Sensor–based pedestrian protection. IS 16(6), 77–81 (2001)

    Google Scholar 

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Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

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© 2007 Springer Berlin Heidelberg

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Gerónimo, D., López, A., Sappa, A.D. (2007). Computer Vision Approaches to Pedestrian Detection: Visible Spectrum Survey. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_70

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  • DOI: https://doi.org/10.1007/978-3-540-72847-4_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72846-7

  • Online ISBN: 978-3-540-72847-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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