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An Introduction to Random Forests for Multi-class Object Detection

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Outdoor and Large-Scale Real-World Scene Analysis

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7474))

Abstract

Object detection in large-scale real-world scenes requires efficient multi-class detection approaches. Random forests have been shown to handle large training datasets and many classes for object detection efficiently. The most prominent example is the commercial application of random forests for gaming [37]. In this paper, we describe the general framework of random forests for multi-class object detection in images and give an overview of recent developments and implementation details that are relevant for practitioners.

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Author information

Authors and Affiliations

  1. Computer Vision Laboratory, ETH Zurich, Switzerland

    Juergen Gall, Nima Razavi & Luc Van Gool

  2. Max Planck Institute for Intelligent Systems, Germany

    Juergen Gall

  3. ESAT/IBBT, Katholieke Universiteit Leuven, Belgium

    Luc Van Gool

Authors
  1. Juergen Gall
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  2. Nima Razavi
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  3. Luc Van Gool
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Editor information

Editors and Affiliations

  1. College of Computing Building, Georgia Institute of Technology, 801 Atlantic Drive, 30332-0280, Atlanta, GA, USA

    Frank Dellaert

  2. Department of Computer Science, University of North Carolina at Chapel Hill, Brooks Computer Science Building, CB#3175, 27599, Chapel Hill, NC, USA

    Jan-Michael Frahm

  3. CVG - Institute of Visual Computing, Department of Computer Science, ETH Zurich, CNB G105, Universitaetstrasse 6, 8092, Zurich, Switzerland

    Marc Pollefeys

  4. Institute for Information Processing (TNT), Leibniz Universität, Appelstr. 9A, 30167, Hannover, Germany

    Laura Leal-Taixé

  5. Institute for Information Processing (TNT), Leibniz Universität, Appelstr. 9A, 30167, Hannover, Germany

    Bodo Rosenhahn

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© 2012 Springer-Verlag Berlin Heidelberg

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Gall, J., Razavi, N., Van Gool, L. (2012). An Introduction to Random Forests for Multi-class Object Detection. In: Dellaert, F., Frahm, JM., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds) Outdoor and Large-Scale Real-World Scene Analysis. Lecture Notes in Computer Science, vol 7474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34091-8_11

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  • DOI: https://doi.org/10.1007/978-3-642-34091-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34090-1

  • Online ISBN: 978-3-642-34091-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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