Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Application of a Decomposed Support Vector Machine Algorithm in Pedestrian Detection from a Moving Vehicle

  • Conference paper
Intelligence and Security Informatics (ISI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3495))

Included in the following conference series:

Abstract

For a shape-based pedestrian detection system [1], the critical requirement for pedestrian detection from a moving vehicle is to both quickly and reliably determine if a moving figure is a pedestrian. This can be achieved by comparing the candidate pedestrian figure with the given pedestrian templates. However, due to the vast number of templates stored, it is difficult to make the matching process fast and reliable. Therefore many pedestrian detection systems [2, 3, 4] re developed to help the matching process. In this paper, we apply a decomposed SVM algorithm in the matching process which can fulfill the recognition task efficiently.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Broggi, A., Bertozzi, M., Fascioli, A., Sechi, M.: Shape-based pedestrian detection. In: Proc. IEEE Intell. Veh. Symp., pp. 215–220 (2000)

    Google Scholar 

  2. Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Proc. Eur. Conf. Comp., vol. 2, pp. 37–49 (2000)

    Google Scholar 

  3. Zhao, L., Thorpe, C.: Stereo-and neural network-based pedestrian detection. In: Procs. IEEE Intl. Conf. on Intelligent Transportation System 1999, Tokyo, Japan, October 1999, pp. 298–303 (1999)

    Google Scholar 

  4. Papageorgiou, C., Evgeniou, T., Poggio, T.: A trainable pedestrian detection system. In: Proc. IEEE Intelligent Vehicles Symposium 1998, Stuttgart, Germany, October 1998, pp. 241–246 (1998)

    Google Scholar 

  5. Bugres, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)

    Article  Google Scholar 

  6. Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods-Support Vector Learning. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.). MIT Press, Cambridge (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Xianbin. Cao

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China

    Hong Qiao & Fei-Yue Wang

Authors
  1. Hong Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei-Yue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianbin. Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Library and Information Science, Rutgers University,  

    Paul Kantor

  2. School of Communication, Information and Library Studies, Rutgers University, 4 Huntington Street, 08901-1071, New Brunswick, NJ, USA

    Gheorghe Muresan

  3. Artificial Solutions, Altonaer Poststraße 13b, 22767, Hamburg, Germany

    Fred Roberts

  4. MIS Department, University of Arizona, 85721, Tucson, AZ, USA

    Daniel D. Zeng

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Fei-Yue Wang

  6. Department of Management Information Systems, Eller College of Management, The University of Arizona, 85721, AZ, USA

    Hsinchun Chen

  7. College of Computing, Georgia Tech Information Security Center, Georgia Institute of Technology, 801 Atlantic Drive, 30332-0280, Atlanta, GA, USA

    Ralph C. Merkle

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Qiao, H., Wang, FY., Cao, X. (2005). Application of a Decomposed Support Vector Machine Algorithm in Pedestrian Detection from a Moving Vehicle. In: Kantor, P., et al. Intelligence and Security Informatics. ISI 2005. Lecture Notes in Computer Science, vol 3495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427995_97

Download citation

  • DOI: https://doi.org/10.1007/11427995_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25999-2

  • Online ISBN: 978-3-540-32063-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation