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Fast Human Detection in RGB-D Images with Progressive SVM-Classification

  • Domingo Iván Rodríguez González
  • Jean-Bernard Hayet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

In this article, we propose a new, fast approach to detect human beings from RGB-D data, named Progressive Classification. The idea of this method is quite simple: As in several state-of-the-art algorithms, the classification is based on the evaluation of HOG-like descriptors within image test windows, which are divided into a set of blocks. In our method, the evaluation of the set of blocks is done progressively in a particular order, in such a way that the blocks that most contribute to the separability between the human and non-human classes are evaluated first. This permits to make an early decision about the human detection without necessarily reaching the evaluation of all the blocks, and therefore accelerating the detection process. We evaluate our method with different HOG-like descriptors and on a challenging dataset.

Keywords

Depth Information Detection Window Linear Support Vector Machine Hellinger Distance Pedestrian Detection 
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 2014

Authors and Affiliations

  • Domingo Iván Rodríguez González
    • 1
  • Jean-Bernard Hayet
    • 1
  1. 1.Centro de Investigación en Matemáticas (CIMAT)GuanajuatoMéxico

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