A vehicle detection using selective multi-stage features in convolutional neural networks

Won Jae Lee, Dong Sung Pae, Dong Won Kim, Myo Taeg Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Vehicle detection is the most basic and important technology in advanced driver assistant system. Conventional methods do not reflect characteristic information of vehicle images, so they were vulnerable to noise. In order to improve the performance of vehicle detection, this paper proposes a vehicle detection framework using selective multi-stage features in convolutional neural networks. We design the convolutional neural network (CNN) model with 10 layers and use a visualization technique to selectively extract features from the activation feature map in CNN. Our proposed features have the characteristic information of vehicle images and are more robust to noise than traditional appearance based features. We train the Adaboost algorithm using these features to implement a vehicle detector. The result of the experiments proves that our proposed vehicle detection framework has a better performance than other frameworks.

Original languageEnglish
Title of host publicationICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
PublisherIEEE Computer Society
Pages1-3
Number of pages3
Volume2017-October
ISBN (Electronic)9788993215137
DOIs
Publication statusPublished - 2017 Dec 13
Event17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of
Duration: 2017 Oct 182017 Oct 21

Other

Other17th International Conference on Control, Automation and Systems, ICCAS 2017
CountryKorea, Republic of
CityJeju
Period17/10/1817/10/21

Fingerprint

Neural networks
Adaptive boosting
Visualization
Chemical activation
Detectors
Experiments

Keywords

  • Adaboost
  • Advanced driver assistant system
  • CNN
  • Vehicle detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Lee, W. J., Pae, D. S., Kim, D. W., & Lim, M. T. (2017). A vehicle detection using selective multi-stage features in convolutional neural networks. In ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings (Vol. 2017-October, pp. 1-3). IEEE Computer Society. https://doi.org/10.23919/ICCAS.2017.8204413

A vehicle detection using selective multi-stage features in convolutional neural networks. / Lee, Won Jae; Pae, Dong Sung; Kim, Dong Won; Lim, Myo Taeg.

ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October IEEE Computer Society, 2017. p. 1-3.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lee, WJ, Pae, DS, Kim, DW & Lim, MT 2017, A vehicle detection using selective multi-stage features in convolutional neural networks. in ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. vol. 2017-October, IEEE Computer Society, pp. 1-3, 17th International Conference on Control, Automation and Systems, ICCAS 2017, Jeju, Korea, Republic of, 17/10/18. https://doi.org/10.23919/ICCAS.2017.8204413
Lee WJ, Pae DS, Kim DW, Lim MT. A vehicle detection using selective multi-stage features in convolutional neural networks. In ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October. IEEE Computer Society. 2017. p. 1-3 https://doi.org/10.23919/ICCAS.2017.8204413
Lee, Won Jae ; Pae, Dong Sung ; Kim, Dong Won ; Lim, Myo Taeg. / A vehicle detection using selective multi-stage features in convolutional neural networks. ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October IEEE Computer Society, 2017. pp. 1-3
@inproceedings{ea4e0c52482143d88793a88f85dc5d0f,
title = "A vehicle detection using selective multi-stage features in convolutional neural networks",
abstract = "Vehicle detection is the most basic and important technology in advanced driver assistant system. Conventional methods do not reflect characteristic information of vehicle images, so they were vulnerable to noise. In order to improve the performance of vehicle detection, this paper proposes a vehicle detection framework using selective multi-stage features in convolutional neural networks. We design the convolutional neural network (CNN) model with 10 layers and use a visualization technique to selectively extract features from the activation feature map in CNN. Our proposed features have the characteristic information of vehicle images and are more robust to noise than traditional appearance based features. We train the Adaboost algorithm using these features to implement a vehicle detector. The result of the experiments proves that our proposed vehicle detection framework has a better performance than other frameworks.",
keywords = "Adaboost, Advanced driver assistant system, CNN, Vehicle detection",
author = "Lee, {Won Jae} and Pae, {Dong Sung} and Kim, {Dong Won} and Lim, {Myo Taeg}",
year = "2017",
month = "12",
day = "13",
doi = "10.23919/ICCAS.2017.8204413",
language = "English",
volume = "2017-October",
pages = "1--3",
booktitle = "ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - A vehicle detection using selective multi-stage features in convolutional neural networks

AU - Lee, Won Jae

AU - Pae, Dong Sung

AU - Kim, Dong Won

AU - Lim, Myo Taeg

PY - 2017/12/13

Y1 - 2017/12/13

N2 - Vehicle detection is the most basic and important technology in advanced driver assistant system. Conventional methods do not reflect characteristic information of vehicle images, so they were vulnerable to noise. In order to improve the performance of vehicle detection, this paper proposes a vehicle detection framework using selective multi-stage features in convolutional neural networks. We design the convolutional neural network (CNN) model with 10 layers and use a visualization technique to selectively extract features from the activation feature map in CNN. Our proposed features have the characteristic information of vehicle images and are more robust to noise than traditional appearance based features. We train the Adaboost algorithm using these features to implement a vehicle detector. The result of the experiments proves that our proposed vehicle detection framework has a better performance than other frameworks.

AB - Vehicle detection is the most basic and important technology in advanced driver assistant system. Conventional methods do not reflect characteristic information of vehicle images, so they were vulnerable to noise. In order to improve the performance of vehicle detection, this paper proposes a vehicle detection framework using selective multi-stage features in convolutional neural networks. We design the convolutional neural network (CNN) model with 10 layers and use a visualization technique to selectively extract features from the activation feature map in CNN. Our proposed features have the characteristic information of vehicle images and are more robust to noise than traditional appearance based features. We train the Adaboost algorithm using these features to implement a vehicle detector. The result of the experiments proves that our proposed vehicle detection framework has a better performance than other frameworks.

KW - Adaboost

KW - Advanced driver assistant system

KW - CNN

KW - Vehicle detection

UR - http://www.scopus.com/inward/record.url?scp=85044447229&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044447229&partnerID=8YFLogxK

U2 - 10.23919/ICCAS.2017.8204413

DO - 10.23919/ICCAS.2017.8204413

M3 - Conference contribution

AN - SCOPUS:85044447229

VL - 2017-October

SP - 1

EP - 3

BT - ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings

PB - IEEE Computer Society

ER -