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Pedestrian Detection Using HOG Dimension Reducing in Video Surveillance

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)

Abstract

Pedestrian detection draws a mount of attention in these years. However, most of the classify-based pedestrian detection methods are facing huge training samples and high computation complexity. In this paper, it proposed a manifold learning based pedestrian detection method. First, modeling the video surveillance scene via mixed gaussian background model and collecting negative samples from the background images; Second, extract the positive and negative samples histogram of oriented gradients(HOG) features, using the local preserving projection(LPP) for dimensionality reduction; Finally, detecting the pedestrian from the input image under the framework of AdaBoost. Experiments show that the algorithm achieved good results both in speed and accuracy of pedestrian detection.

Keywords

Pedestrian detection Manifold learning HOG AdaBoost 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kebin Huang
    • 1
  • Feng Wang
    • 1
  • Xiaoshuang Xu
    • 1
  • Yun Cheng
    • 1
  1. 1.Department of Digital Media TechnologyHuanggang Normal UniversityHuangzhouChina

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