Implementation of a Cascaded Histogram of Oriented Gradient (HOG)-Based Pedestrian Detector

Abstract

In this report, we present our implementation of a cascaded Histogram of Oriented Gradient (HOG) based pedestrian detector. Most human detection algorithms can be implemented as a cascade of classifiers to decrease computation time while maintaining approximately the same performance. Although cascaded versions of Dalal and Triggs s HOG detector already exist, we aim to provide a more detailed explanation of an implementation than is currently available. We also use Asymmetric Boosting instead of Adaboost to train the cascade stages. We show that this approach reduces the number of weak classifiers needed per stage. We present the results of our detector on the INRIA pedestrian detection dataset and compare them to the results provided by Dalal and Triggs.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA586027

Entities

People

  • Alex Chan
  • Christopher Reale
  • Prudhvi Gurram
  • Shuowen Hu

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Computer Programs
  • Detection
  • Detectors
  • False Alarms
  • Feature Extraction
  • Histograms
  • Machine Learning
  • Supervised Machine Learning
  • Unmanned Vehicles
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Computer Vision.
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