Global template matching for guiding the learning of human detector

Shih Shinh Huang*, Yao Ming Yu, Chien Yi Mao, Pei Yung Hsiao, Lu An Yen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This work investigates a semantic-driven human detection algorithm, which employs global human template matching to inspire the local features based Adaboosting algorithm. We use distance transform to analyze distances between training samples and human contour template to obtain a classifier based on human outline features. At the training stage, the global outline feature will be coordinated into the Adaboost framework to guide the learning of a set of HOGs local classifiers. In other words, we make a stronger human classifier by dynamically tuning the hyper-plane of the support vector machine to combine both global and local features for attaining a better accuracy. A global fused error rate is also proposed to enhance the modified Adaboost iterative calculation such that a human detector called strong classifier can be obtained. The experiments illustrate that the detection rate received from the presented framework is above 20% better than original HOGs-based human detector.

Original languageEnglish
Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages565-570
Number of pages6
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 14 10 201217 10 2012

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period14/10/1217/10/12

Keywords

  • Adaboost
  • Distance transform
  • HOGs
  • Human detection
  • SVM
  • Template Matching

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