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Asian Conference on Computer Vision

ACCV 2014: Computer Vision – ACCV 2014 pp 290–306Cite as

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

Abstract

Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with several hand-designed components (e.g., thresholds). Such a strategy inherently suffers from several shortcomings, such as the inability to detect nearby objects. In this paper, we try to alleviate these problems and explore a novel formulation of NMS as a well-defined clustering problem. Our method builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars. Contrary to the greedy approach, our method is solved globally and its parameters can be automatically learned from training data. In experiments, we show in two contexts – object class and generic object detection – that it provides a promising solution to the shortcomings of the greedy NMS.

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Acknowledgement

The authors gratefully acknowledge support by Toyota.

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Authors and Affiliations

  1. Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland

    Rasmus Rothe, Matthieu Guillaumin & Luc Van Gool

  2. ESAT - PSI/IBBT, K.U. Leuven, Leuven, Belgium

    Luc Van Gool

Authors
  1. Rasmus Rothe
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  2. Matthieu Guillaumin
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  3. Luc Van Gool
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Corresponding author

Correspondence to Rasmus Rothe .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Rothe, R., Guillaumin, M., Van Gool, L. (2015). Non-maximum Suppression for Object Detection by Passing Messages Between Windows. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

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