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 Research on Human Detection in Outdoor Scene Based on Generalized Hough Transform

  

  1. Software Technology Institute of Dalian Jiaotong University, Dalian 116052, China
  • Received:2014-12-22 Online:2015-04-27 Published:2015-04-29

Abstract:

 A novel outdoor scene human detection approach based on generalized Hough transform is proposed in this paper. Firstly, we randomly extract fragments of human
instances from annotated training images and build a fragment dictionary. Then we utilize these extracted fragments to compute feature vectors for each training image. In order
to rapidly detect human in a static image, Gentleboost algorithm is used to train detectors. In each round of boosting, a regression decision stump is learned as the weak
classifier which can pick the most distinctive fragment features from the high dimensional fragment feature vector. When running the trained human detector, all the weak
classifiers voted for the possible positions of human instances in a given test image. Finally, the output positions of all the weak classifiers are accumulated and some low
score outputs are eliminated using a manually specified threshold to get the final detection outputs. Experiments on LabelMe datasets show that this approach can rapidly detect
human instances from static image using relative fewer training images and can effectively solve the partial occlusion problem.

Key words:  human detection, generalized Hough transform, fragment features, outdoor scene