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

doi:10.1109/AVSS.2006.30

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 proceeding › Conference contribution › peer-review

22 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 language	English
Title of host publication	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006
Publisher	IEEE Computer Society
Pages	15-20
Number of pages	6
ISBN (Print)	0769526888, 9780769526881
DOIs	https://doi.org/10.1109/AVSS.2006.30
Publication status	Published - 2006
Externally published	Yes
Event	IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006 - Sydney, NSW, Australia Duration: 22 Nov 2006 → 24 Nov 2006

Publication series

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

Conference

Conference	IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006
Country/Territory	Australia
City	Sydney, NSW
Period	22/11/06 → 24/11/06

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/AVSS.2006.30

Cite this

Wu, Q., Zhang, H., Jia, W., He, X., Yang, J., & Hintz, T. (2006). Car plate detection using cascaded tree-style learner based on hybrid object features. In Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006 (pp. 15-20). Article 4020674 (Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006). IEEE Computer Society. https://doi.org/10.1109/AVSS.2006.30

Wu, Qiang ; Zhang, Huaifeng ; Jia, Wenjing et al. / Car plate detection using cascaded tree-style learner based on hybrid object features. Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006. IEEE Computer Society, 2006. pp. 15-20 (Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006).

@inproceedings{f55898cb8a44446aa5ed45c91d139c2d,

title = "Car plate detection using cascaded tree-style learner based on hybrid object features",

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.",

author = "Qiang Wu and Huaifeng Zhang and Wenjing Jia and Xiangjian He and Jie Yang and Tom Hintz",

year = "2006",

doi = "10.1109/AVSS.2006.30",

language = "English",

isbn = "0769526888",

series = "Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006",

publisher = "IEEE Computer Society",

pages = "15--20",

booktitle = "Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006",

address = "United States",

note = "IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006 ; Conference date: 22-11-2006 Through 24-11-2006",

}

Wu, Q, Zhang, H, Jia, W, He, X, Yang, J & Hintz, T 2006, Car plate detection using cascaded tree-style learner based on hybrid object features. in Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006., 4020674, Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, IEEE Computer Society, pp. 15-20, IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, Sydney, NSW, Australia, 22/11/06. https://doi.org/10.1109/AVSS.2006.30

Car plate detection using cascaded tree-style learner based on hybrid object features. / Wu, Qiang; Zhang, Huaifeng; Jia, Wenjing et al.
Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006. IEEE Computer Society, 2006. p. 15-20 4020674 (Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Car plate detection using cascaded tree-style learner based on hybrid object features

AU - Wu, Qiang

AU - Zhang, Huaifeng

AU - Jia, Wenjing

AU - He, Xiangjian

AU - Yang, Jie

AU - Hintz, Tom

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

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U2 - 10.1109/AVSS.2006.30

DO - 10.1109/AVSS.2006.30

M3 - Conference contribution

AN - SCOPUS:34547327601

SN - 0769526888

SN - 9780769526881

T3 - Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006

SP - 15

EP - 20

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

PB - IEEE Computer Society

T2 - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006

Y2 - 22 November 2006 through 24 November 2006

ER -

Wu Q, Zhang H, Jia W, He X, Yang J, Hintz T. Car plate detection using cascaded tree-style learner based on hybrid object features. In Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006. IEEE Computer Society. 2006. p. 15-20. 4020674. (Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006). doi: 10.1109/AVSS.2006.30

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

Abstract

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ASJC Scopus subject areas

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