Multiple lane boundary detection using a combination of low-level image features
ITSC（2014）
摘要
In this paper, we propose a vision-based multiple lane boundaries detection and estimation structure that fuses the edge features and the high intensity features. Our approach utilizes a camera as the only input sensor. The application of Kalman filter for information fusion and tracking significantly improves the reliability and robustness of our system. We test our system on roads with different driving scenarios, including day, night, heavy traffic, rain, confusing textures and shadows. The feasibility of our approach is demonstrated by quantitative evaluation using manually labeled video clips.
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关键词
kalman filters,cameras,driver information systems,edge detection,image fusion,image sensors,image texture,object tracking,video signal processing,kalman filter,camera,confusing textures,day,driving scenarios,edge feature fusion,estimation structure,heavy traffic,high intensity feature fusion,information fusion,low-level image features,manually labeled video clips,night,rain,sensor,shadows,vision-based multiple lane boundary detection,boundaries,kalman filtering,feature extraction,image processing
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