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 |
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Article number | 2468 |
Journal | Applied Sciences (Switzerland) |
Volume | 8 |
Issue number | 12 |
DOIs | |
Publication status | Published - 2018 Dec 3 |
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