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Cascaded Reduction and Growing of Result Sets for Combining Object Detectors

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Multiple Classifier Systems (MCS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7872))

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Abstract

In this paper cascaded reduction and growing of result sets is introduced as a principle for combining the results of different object detectors. First, different candidate operating points are selected for each object detection algorithm. This procedure is based on the analysis of precision and recall of the individual methods. Selecting an appropriate operating point prior to fusion is important because it regulates the cardinal number of the result set. As diversity and correlation between object detectors also depend on the elements of the result sets, this and the application of set operations allow to create a final set of detected objects by including missing and excluding false detections. The approach allows both diverse and correlated detectors to contribute to the performance of the combined detector. The performance of the proposed algorithm is compared to other combining algorithms. It outperforms or competes with existing state of the art combiners for several datasets. Additionally, the results provide a significant improvement in the interpretability of the combining rules. As a unique feature of the proposed algorithm, the found operating points can be used to reconfigure the object detection algorithms to adapt their individual results to the needs of the combination procedure allowing a reduction in runtime.

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Author information

Authors and Affiliations

  1. Humboldt University, Unter den Linden 6, 10099, Berlin, Germany

    Uwe Knauer

  2. Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany

    Uwe Knauer & Udo Seiffert

Authors
  1. Uwe Knauer
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  2. Udo Seiffert
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Editor information

Editors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, 210046, Nanjing, China

    Zhi-Hua Zhou

  2. Dept. of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

  3. Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK

    Josef Kittler

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Knauer, U., Seiffert, U. (2013). Cascaded Reduction and Growing of Result Sets for Combining Object Detectors. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_11

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  • DOI: https://doi.org/10.1007/978-3-642-38067-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38066-2

  • Online ISBN: 978-3-642-38067-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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