Car plate detection using cascaded tree-style learner based on hybrid object features

Qiang Wu, Huaifeng Zhang, Wenjing Jia, Xiangjian He, Jie Yang, Tom Hintz

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

19 Citations (Scopus)

Abstract

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006
PublisherIEEE Computer Society
Pages15-20
Number of pages6
ISBN (Print)0769526888, 9780769526881
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventIEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006 - Sydney, NSW, Australia
Duration: 22 Nov 200624 Nov 2006

Publication series

NameProceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006

Conference

ConferenceIEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006
Country/TerritoryAustralia
CitySydney, NSW
Period22/11/0624/11/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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