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Recursive Coarse-to-Fine Localization for Fast Object Detection

  • Marco Pedersoli
  • Jordi Gonzàlez
  • Andrew D. Bagdanov
  • Juan J. Villanueva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

Abstract

Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter.

Keywords

Object Detection Object Model Resolution Level Human Detection Feature Resolution 
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.

Supplementary material

978-3-642-15567-3_21_MOESM1_ESM.avi (8.9 mb)
Electronic Supplementary Material (9,139 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marco Pedersoli
    • 1
  • Jordi Gonzàlez
    • 1
  • Andrew D. Bagdanov
    • 1
  • Juan J. Villanueva
    • 1
  1. 1.Dept. Ciències de la Computació & Centre de Visió per ComputadorEdifici O, Campus UAB 08193 Bellaterra (Cerdanyola)BarcelonaSpain

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