In this paper, we propose an integrated system that combines vehicle detection, lane detection, and vehicle distance estimation in a collaborative manner. Adaptive search windows for vehicles provide constraints on the width between lanes. By exploiting the constraints, the search space for lane detection can be efficiently reduced. We employ local patch constraints for lane detection to improve the reliability of lane detection. Moreover, it is challenging to estimate the vehicle distance from images/videos captured form monocular camera in real time. In our approach, we utilize lane marker with the associated 3D constraint to estimate the camera pose and the distances to frontal vehicles. Experimental results on real videos show that the proposed system is robust and accurate in terms of vehicle and lane detection and vehicle distance estimation.
CITATION STYLE
Chen, Y. C., Su, T. F., & Lai, S. H. (2015). Integrated vehicle and lane detection with distance estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9010, pp. 473–485). Springer Verlag. https://doi.org/10.1007/978-3-319-16634-6_35
Mendeley helps you to discover research relevant for your work.