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Cleaning Up Multiple Detections Caused by Sliding Window Based Object Detectors

  • Arne Ehlers
  • Björn Scheuermann
  • Florian Baumann
  • Bodo Rosenhahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Object detection is an important and challenging task in computer vision. In cascaded detectors, a scanned image is passed through a cascade in which all stage detectors have to classify a found object positively. Common detection algorithms use a sliding window approach, resulting in multiple detections of an object. Thus, the merging of multiple detections is a crucial step in post-processing which has a high impact on the final detection performance. First, this paper proposes a novel method for merging multiple detections that exploits intra-cascade confidences using Dempster’s Theory of Evidence. The evidence theory allows hereby to model confidence and uncertainty information to compute the overall confidence measure for a detection. Second, this confidence measure is applied to improve the accuracy of the determined object position. The proposed method is evaluated on public object detection benchmarks and is shown to improve the detection performance.

Keywords

Detection Performance Mass Function True Positive Rate Face Detection Evidence Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arne Ehlers
    • 1
  • Björn Scheuermann
    • 1
  • Florian Baumann
    • 1
  • Bodo Rosenhahn
    • 1
  1. 1.Institut für Informationsverarbeitung (TNT)Leibniz Universität HannoverGermany

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