Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection
- Authors
- Lee, Won-Jae; Kim, Dong W.; Kang, Tae-Koo; Lim, Myo-Taeg
- Issue Date
- 12월-2018
- Publisher
- MDPI
- Keywords
- vehicle detection; feature extraction; convolutional neural network; AdaBoost
- Citation
- APPLIED SCIENCES-BASEL, v.8, no.12
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 8
- Number
- 12
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/71365
- DOI
- 10.3390/app8122468
- ISSN
- 2076-3417
- 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 algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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