Pedestrian Detection in Poor Visibility Conditions: Would SWIR Help?

  • Massimo Bertozzi
  • Rean Isabella Fedriga
  • Alina Miron
  • Jean-Luc Reverchon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


The 2WIDE_SENSE (WIDE spectral band & WIDE dynamics multifunctional imaging SENSor Enabling safer car transportation) EU funded project is aimed at the development of a low-cost camera sensor for Advanced Driver Assistance Systems (ADAS) applications able to acquire the full visible to Short Wave InfraRed (SWIR) spectrum from 400 to 1700 nm. This paper presents the first results obtained by investigating the SWIR contribution to pedestrian detection in difficult visibility conditions as haze and fog employing the wide-bandwidth camera developed within the project.


SWIR pedestrian detection classification large bandwidth cameras haze fog 


  1. 1.
    Bertozzi, M., Fedriga, R.I., Miron, A., Reverchon, J.-L.: SWIR vs. Visible Imagers for Pedestrian Detection in Reduced Visibility Conditions. In: Procs. IEEE Intl. Conf. on Intelligent Transportation Systems, The Hague, Nederlands (submitted)Google Scholar
  2. 2.
    Binelli, E., Broggi, A., Fascioli, A., Ghidoni, S., Grisleri, P., Graf, T., Meinecke, M.-M.: A Modular Tracking System for Far Infrared Pedestrian Recognition. In: Procs. IEEE Intelligent Vehicles Symposium 2005, Las Vegas, USA, pp. 758–763 (June 2005)Google Scholar
  3. 3.
    Brooks, A.L.: Improved Multispectral Skin Detection and Its Application to Search Space Reduction for Dismount Detection Based on Histograms of Oriented Gradients. Master’s thesis, Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA (October 2012)Google Scholar
  4. 4.
    Chang, H., Koschan, A., Abidi, M.: Multispectral visible and infrared imaging for face. In: Procs. IEEE Computer Vision and Pattern Recognition Workshops, pp. 1–6. IEEE Computer Society, Anchorage (2008)Google Scholar
  5. 5.
    Everingham, M., van Gool, L., Williams, C., Winn, J., Zisserman, A.: The Pascal visual object classes,
  6. 6.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Cascade object detection with deformable part models. In: Procs. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2241–2248 (2010)Google Scholar
  7. 7.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  8. 8.
    Hansen, M.P., Malchow, D.S.: Overview of SWIR detectors, cameras, and applications. In: Procs. SPIE, vol. 6939, Thermosense XXX (March 2008)Google Scholar
  9. 9.
    Kilgore, G.A., Whillock, P.R.: Skin Detection Sensor, United States Patent Office, Publication nr. US2007/0106160A1, Application n. 11/264,654, Issued patent US7446316, 2008-11-04 (November 2008)Google Scholar
  10. 10.
    Malchow, D.: NIR Trends: Penetrating The Haze Of Scattered Light. In: UTC Aerospace Systems (Sensors Unlimited Products) Goodrich Corporation (October 2008)Google Scholar
  11. 11.
    Nunez, A.S., Mendenhall, M.J.: Detection of Human Skin in Near Infrared Hyperspectral Imagery. In: Procs. IEEE Geoscience and Remote Sensing Symposium, pp. 621–624. IEEE Computer Society (July 2008)Google Scholar
  12. 12.
    Valldorf, J., Gessner, W. (eds.): Advanced Microsystems for Automotive Applications 2005. Springer, Berlin (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Massimo Bertozzi
    • 1
  • Rean Isabella Fedriga
    • 1
  • Alina Miron
    • 2
  • Jean-Luc Reverchon
    • 3
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità di ParmaItaly
  2. 2.INSA de RouenSaint-Étienne-du-Rouvray CedexFrance
  3. 3.III-V LaboratoireRoute de NozayMarcoussis CedexFrance

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