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Application of Cooperative Co-evolution in Pedestrian Detection Systems

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Intelligence and Security Informatics (ISI 2005)

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

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Abstract

In general, a shape-based[1] pedestrian detection system includes the following two steps:

(a) finding out and tracking a possible pedestrian figure, and

(b) determining if the candidate pedestrian figure is really a pedestrian figure by checking if it matches with any of the pedestrian templates.

Since there are a large number of templates, it is necessary to build up a search tree for the Match process [2,3]. Each node in the tree is one feature of the corresponding templates that can be used for classification and where each branch is one pedestrian template. Usually, the search tree is not adjustable during the matching process.

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References

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

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

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  3. Gavrila, D.M., Giebel, J., Munder, S.: Vision-based pedestrian detection: the PROTECTOR system. IEEE IVS 2004 (2004)

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  4. Emlen, J.M.: Population biology: the co-evolution of population dynamics and behaviors. Macmillan Publishing Company, New York (1984)

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  5. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)

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Author information

Authors and Affiliations

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

    Xianbin Cao & Xinzheng Zhang

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

    Hong Qiao & Fei-Yue Wang

Authors
  1. Xianbin Cao
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  2. Hong Qiao
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  3. Fei-Yue Wang
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  4. Xinzheng Zhang
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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

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© 2005 Springer-Verlag Berlin Heidelberg

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Cao, X., Qiao, H., Wang, FY., Zhang, X. (2005). Application of Cooperative Co-evolution in Pedestrian Detection Systems. 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_98

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  • DOI: https://doi.org/10.1007/11427995_98

  • 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)

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