Abstract
Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithmusing these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.
| Original language | English |
|---|---|
| Article number | 2468 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 8 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2018 Dec 3 |
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Keywords
- AdaBoost
- Convolutional neural network
- Feature extraction
- Vehicle detection
ASJC Scopus subject areas
- Materials Science(all)
- Instrumentation
- Engineering(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes
Cite this
Convolution neural network with selective multi-stage feature fusion : Case study on vehicle rear detection. / Lee, Won Jae; Kim, Dong W.; Kang, Tae Koo; Lim, Myo Taeg.
In: Applied Sciences (Switzerland), Vol. 8, No. 12, 2468, 03.12.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Convolution neural network with selective multi-stage feature fusion
T2 - Case study on vehicle rear detection
AU - Lee, Won Jae
AU - Kim, Dong W.
AU - Kang, Tae Koo
AU - Lim, Myo Taeg
PY - 2018/12/3
Y1 - 2018/12/3
N2 - Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithmusing these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.
AB - Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithmusing these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.
KW - AdaBoost
KW - Convolutional neural network
KW - Feature extraction
KW - Vehicle detection
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UR - http://www.scopus.com/inward/citedby.url?scp=85057842574&partnerID=8YFLogxK
U2 - 10.3390/app8122468
DO - 10.3390/app8122468
M3 - Article
VL - 8
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
SN - 2076-3417
IS - 12
M1 - 2468
ER -