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Cascaded Confidence Filtering for Improved Tracking-by-Detection

  • Severin Stalder
  • Helmut Grabner
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

We propose a novel approach to increase the robustness of object detection algorithms in surveillance scenarios. The cascaded confidence filter successively incorporates constraints on the size of the objects, on the preponderance of the background and on the smoothness of trajectories. In fact, the continuous detection confidence scores are analyzed locally to adapt the generic detector to the specific scene. The approach does not learn specific object models, reason about complete trajectories or scene structure, nor use multiple cameras. Therefore, it can serve as preprocessing step to robustify many tracking-by-detection algorithms. Our real-world experiments show significant improvements, especially in the case of partial occlusions, changing backgrounds, and similar distractors.

Keywords

Object Detection Ground Plane Background Model Human Detection Scene Structure 
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 2010

Authors and Affiliations

  • Severin Stalder
    • 1
  • Helmut Grabner
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.Computer Vision LaboratoryETH ZurichSwitzerland
  2. 2.ESAT - PSI / IBBTK.U. LeuvenBelgium

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