Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm

Yeong Kang Lai; Yu Hsi Chou; Thomas Schumann

doi:10.1109/ICCE-Berlin.2017.8210585

Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm

Yeong Kang Lai, Yu Hsi Chou, Thomas Schumann

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

10 Scopus citations

Abstract

This paper proposed a monocular vehicle detection for forward collision warning system. We use the active-learning framework to train a cascade classifier and use a two steps vehicle detection. We used five test data to quantify our detection performance, analyzing the two-stage vehicle detection improvement, and the overall detection rate and the false detection rate. In a good light condition, the detection rate and the false detection rate can achieve 0.967 and 0.122, respectively. Our system can achieve up to 45 frames per second on Intel core Ì7-6700 CPU.

Original language	English
Title of host publication	2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017
Publisher	IEEE Computer Society
Pages	47-48
Number of pages	2
ISBN (Electronic)	9781509040148
DOIs	https://doi.org/10.1109/ICCE-Berlin.2017.8210585
State	Published - 14 12 2017
Externally published	Yes
Event	7th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017 - Berlin, Germany Duration: 03 09 2017 → 06 09 2017

Publication series

Name	IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Volume	2017-September
ISSN (Print)	2166-6814
ISSN (Electronic)	2166-6822

Conference

Conference	7th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017
Country/Territory	Germany
City	Berlin
Period	03/09/17 → 06/09/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Access to Document

10.1109/ICCE-Berlin.2017.8210585

Cite this

Lai, Y. K., Chou, Y. H., & Schumann, T. (2017). Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm. In 2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017 (pp. 47-48). (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICCE-Berlin.2017.8210585

Lai, Yeong Kang ; Chou, Yu Hsi ; Schumann, Thomas. / Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm. 2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017. IEEE Computer Society, 2017. pp. 47-48 (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin).

@inproceedings{e09fa9470f6645e8a3f29e53ec4107b8,

title = "Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm",

abstract = "This paper proposed a monocular vehicle detection for forward collision warning system. We use the active-learning framework to train a cascade classifier and use a two steps vehicle detection. We used five test data to quantify our detection performance, analyzing the two-stage vehicle detection improvement, and the overall detection rate and the false detection rate. In a good light condition, the detection rate and the false detection rate can achieve 0.967 and 0.122, respectively. Our system can achieve up to 45 frames per second on Intel core {\`I}7-6700 CPU.",

author = "Lai, {Yeong Kang} and Chou, {Yu Hsi} and Thomas Schumann",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017 ; Conference date: 03-09-2017 Through 06-09-2017",

year = "2017",

month = dec,

day = "14",

doi = "10.1109/ICCE-Berlin.2017.8210585",

language = "英语",

series = "IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin",

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booktitle = "2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017",

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}

Lai, YK, Chou, YH & Schumann, T 2017, Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm. in 2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017. IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, vol. 2017-September, IEEE Computer Society, pp. 47-48, 7th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017, Berlin, Germany, 03/09/17. https://doi.org/10.1109/ICCE-Berlin.2017.8210585

Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm. / Lai, Yeong Kang; Chou, Yu Hsi; Schumann, Thomas.
2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017. IEEE Computer Society, 2017. p. 47-48 (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; Vol. 2017-September).

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

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PY - 2017/12/14

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N2 - This paper proposed a monocular vehicle detection for forward collision warning system. We use the active-learning framework to train a cascade classifier and use a two steps vehicle detection. We used five test data to quantify our detection performance, analyzing the two-stage vehicle detection improvement, and the overall detection rate and the false detection rate. In a good light condition, the detection rate and the false detection rate can achieve 0.967 and 0.122, respectively. Our system can achieve up to 45 frames per second on Intel core Ì7-6700 CPU.

AB - This paper proposed a monocular vehicle detection for forward collision warning system. We use the active-learning framework to train a cascade classifier and use a two steps vehicle detection. We used five test data to quantify our detection performance, analyzing the two-stage vehicle detection improvement, and the overall detection rate and the false detection rate. In a good light condition, the detection rate and the false detection rate can achieve 0.967 and 0.122, respectively. Our system can achieve up to 45 frames per second on Intel core Ì7-6700 CPU.

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Lai YK, Chou YH, Schumann T. Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm. In 2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017. IEEE Computer Society. 2017. p. 47-48. (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin). doi: 10.1109/ICCE-Berlin.2017.8210585

Vehicle detection for forward collision warning system based on a cascade classifier using adaboost algorithm

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