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Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection

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Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

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Abstract

We present an example-based algorithm for detecting objects in images by integrating component-based classifiers, which automaticaly select the best feature for each classifier and are combined according to the AdaBoost algorithm. The system employs a soft-margin SVM for the base learner, which is trained for all features and the optimal feature is selected at each stage of boosting. We employed two features such as a histogram-equalization and an edge feature in our experiment. The proposed method was applied to the MIT CBCL pedestrian image database, and 100 sub-regions were extracted from each image as local-features. The experimental results showed fairly good classification ratio with selecting sub-regions, while some improvement attained by combining the two features, histogram-equalization and edge. However, the combination of features could to select good local-features for base learners.

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Authors and Affiliations

  1. Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568, Japan

    Kenji Nishida & Takio Kurita

Authors
  1. Kenji Nishida
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  2. Takio Kurita
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Editor information

Editors and Affiliations

  1. NASA Ames Research Center, Mail Stop 269-1, 94035-1000, Moffett Field, CA, USA

    Nikunj C. Oza

  2. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ, USA

    Robi Polikar

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

    Josef Kittler

  4. University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2005 Springer-Verlag Berlin Heidelberg

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Nishida, K., Kurita, T. (2005). Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_3

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  • DOI: https://doi.org/10.1007/11494683_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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