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Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

Determining the optimal parameters for a given detection algorithm is not straightforward and what ends up as the final values is mostly based on experience and heuristics. In this paper we investigate the influence of three basic parameters in the widely used Aggregate Channel Features (ACF) object detector applied for traffic light detection. Additionally, we perform an exhaustive search for the optimal parameters for the night time data from the LISA Traffic Light Dataset. The optimized detector reaches an Area-Under-Curve of 66.63% on calculated precision-recall curve.

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Authors and Affiliations

  1. Visual Analysis of People Laboratory, Aalborg University, Aalborg, Denmark

    Morten B. Jensen, Mark P. Philipsen & Thomas B. Moeslund

  2. Computer Vision and Robotics Research Laboratory, UC San Diego, La Jolla, USA

    Morten B. Jensen, Mark P. Philipsen & Mohan Trivedi

Authors
  1. Morten B. Jensen
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  2. Mark P. Philipsen
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  3. Thomas B. Moeslund
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  4. Mohan Trivedi
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Corresponding author

Correspondence to Morten B. Jensen .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Jensen, M.B., Philipsen, M.P., Moeslund, T.B., Trivedi, M. (2016). Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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