CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Camera calibration for road applications
Computer Vision and Image Understanding
Using Robust Estimation Algorithms for Tracking Explicit Curves
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Active Contour Road Model for Smart Vehicle
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A lane-curve detection based on an LCF
Pattern Recognition Letters
Computer vision techniques for traffic flow computation
Pattern Analysis & Applications
Accurate road following and reconstruction by computer vision
IEEE Transactions on Intelligent Transportation Systems
Color-based road detection in urban traffic scenes
IEEE Transactions on Intelligent Transportation Systems
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
Linear fuzzy space based road lane model and detection
Knowledge-Based Systems
A real-time system of lane detection and tracking based on optimized RANSAC B-spline fitting
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Hierarchical fuzzy logic based approach for object tracking
Knowledge-Based Systems
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We present a robust road detection and tracking method using multiple vanishing points and the condensation filter. We represent the road using an extended hyperbola model with an added non-linear term to handle transitions between straight and curved road segments. The parameters of the road model are estimated using multiple vanishing points located in road segments. A vanishing line is then determined using a robust iterative curve fitting technique to recover parameters of the road model. These are then fed into a robust condensation tracker [M. Isard, A. Blake, Condensation: conditional density propagation for visual tracking, Int. J. Comput. Vision, 1998] to track the road. The tracker is able to deal with difficult road conditions. Experiments using real road videos demonstrate the suitability of our approach for real-time applications. A comparison with the Kalman filtering technique demonstrates the robustness of our approach.