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Towards Automated Learning of Object Detectors

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Applications of Evolutionary Computation (EvoApplications 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6024))

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Abstract

Recognizing arbitrary objects in images or video sequences is a difficult task for a computer vision system. We work towards automated learning of object detectors from video sequences (without user interaction). Our system uses object motion as an important cue to detect independently moving objects in the input sequence. The largest object is always taken as the teaching input, i.e. the object to be extracted. We use Cartesian Genetic Programming to evolve image processing routines which deliver the maximum output at the same position where the detected object is located. The graphics processor (GPU) is used to speed up the image processing. Our system is a step towards automated learning of object detectors.

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Author information

Authors and Affiliations

  1. Eberhard-Karls-Universität Tübingen, Wilhelm-Schickard-Institut für Informatik, Abt. Rechnerarchitektur, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

Authors
  1. Marc Ebner
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  3. Departmento Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  4. Wilhelm Schickard-Institut für Informatik, Sand 1, Universität Tübingen, 72076, Tübingen, Germany

    Marc Ebner

  5. Computer Science, Aston Triangle, Aston University, B4 7ET, Birmingham, UK

    Anikó Ekárt

  6. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Edif. 8G Acceso B, 46022, Valencia, Spain

    Anna I. Esparcia-Alcazar

  7. Advanced Technology Centre Rolls-Royce, Singapore, Singapore

    Chi-Keong Goh

  8. Departamento de Electrónica y Tnologia de los Computadores, University of Granada, 18071, Grenada, Spain

    Juan J. Merelo

  9. Department of Mathematical Information Technology,, University of Jyväskylä, 4th Floor, P.O. Box 25, 40014, Agora, Finland

    Ferrante Neri

  10. Lehrstuhl für Algorithm Engineering, TU Dortmund, Otto-Hahn-Straße 14, 44227, Dortmund, Germany

    Mike Preuß

  11. Center for Computer Games Research, IT University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen S, Denmark

    Julian Togelius  & Georgios N. Yannakakis  & 

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Ebner, M. (2010). Towards Automated Learning of Object Detectors. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_24

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  • DOI: https://doi.org/10.1007/978-3-642-12239-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12238-5

  • Online ISBN: 978-3-642-12239-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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