Pedestrian Detection Using Boosted Co-Occurrence Edge Features

We developed a pedestrian detection system in order to notice a driver the existence of pedestrians. Our previous system which using HOG adaboost classifiers can detect pedestrians with high speed, however, due to the limitation of HOG features, it still has a high false positive rate. In this paper, we propose to cascade the previous pedestrian detector by a false positive remover to improve the accuracy while keeping the high speed. The proposed false positive remover is composed of two adaboost classifiers, one for grayscale image, and another for distance image. The classifier for grayscale image utilize boosted spatial co-occurrence matrix of edge directions --- a kind of improved edge feature proposed in this paper. The classifier for distance image boosts shape features together with spatial co-occurrence matrix of edge directions extracted from the distance image, which is obtained from a stereo camera. Experimental results are presented to validate our method.


  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; Photos; References;
  • Pagination: 10p
  • Monograph Title: ITS Connections: Saving Time. Saving Lives

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Filing Info

  • Accession Number: 01144957
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 17 2009 9:21AM