Abstract
Despite significant progress in vehicle detection over the last few decades, vehicle detection performance in heavy traffic is still inadequate. In this paper, we propose a new algorithm for vehicle detection in heavy traffic to improve detection performance. It uses two proposed segmentation methods, namely, the disparity map-based bird's-eye-view mapping segmentation method and the edge distance weighted conditional random field (CRF)-based segmentation method. Our experimental results show that the proposed algorithm outperforms conventional algorithms. The improvements in performance range from 10.8 % to 20.5 % increase in F-measure.
Original language | English |
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Pages (from-to) | 54-58 |
Number of pages | 5 |
Journal | Elektronika ir Elektrotechnika |
Volume | 20 |
Issue number | 9 |
DOIs | |
State | Published - 2014 |
Keywords
- Image segmentation
- Intelligent vehicle
- Object recognition
- Stereo image processing