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The treelike assembly classifier for pedestrian detection

  • C. X. Wei*
  • , X. B. Cao
  • , Y. W. Xu
  • , Hong Qiao
  • , Fei Yue Wang
  • *Corresponding author for this work
  • University of Science and Technology of China
  • Anhui Province Key Laboratory of Software in Computing and Communication
  • Chinese Academy of Sciences

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

Abstract

Until now, classification is a primary technology in Pedestrian Detection. However, most existing single-classifiers and cascaded classifiers can hardly satisfy practical needs (e.g. false negative rate, false positive rate and detection speed). In this paper, we proposed an assembly classifier which was specifically designed for pedestrian detection in order to get higher detection rate and lower false positive rate at high speed. The assembly classifier is trained to select out the best single-classifiers, all of which will be arranged in a proper structure; finally, a treelike classifier is obtained. The experimental results have validated that the proposed assembly classifier generates better results than most of the existing single-classifiers and cascaded classifiers.

Original languageEnglish
Title of host publicationIntelligence and Security Informatics - Pacific Asia Workshop, PAISI 2007, Proceedings
PublisherSpringer Verlag
Pages232-237
Number of pages6
ISBN (Print)9783540715481
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2007 - Chengdu, China
Duration: 11 Apr 200712 Apr 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4430 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2007 Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2007
Country/TerritoryChina
CityChengdu
Period11/04/0712/04/07

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