Realize a mobile lane detection system based on pocket PC portable devices
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Road Lane Detection with Elimination of High-Curvature Edges
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
A robust lane detection and verification method for intelligent vehicles
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Extraction of contextual information for automotive applications
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
CyberC3: a prototype cybernetic transportation system for urban applications
IEEE Transactions on Intelligent Transportation Systems
A real-time versatile roadway path extraction and tracking on an FPGA platform
Computer Vision and Image Understanding
A novel system for robust lane detection and tracking
Signal Processing
A robust lane detection approach based on MAP estimate and particle swarm optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Linear fuzzy space based road lane model and detection
Knowledge-Based Systems
Hi-index | 0.00 |
This work presents the current status of the Springrobot autonomous vehicle project, whose main objective is to develop a safety-warning and driver-assistance system and an automatic pilot for rural and urban traffic environments. This system uses a high precise digital map and a combination of various sensors. The architecture and strategy for the system are briefly described and the details of lane-marking detection algorithms are presented. The R and G channels of the color image are used to form graylevel images. The size of the resulting gray image is reduced and the Sobel operator with a very low threshold is used to get a grayscale edge image. In the adaptive randomized Hough transform, pixels of the gray-edge image are sampled randomly according to their weights corresponding to their gradient magnitudes. The three-dimensional (3-D) parametric space of the curve is reduced to the two-dimensional (2-D) and the one-dimensional (1-D) space. The paired parameters in two dimensions are estimated by gradient directions and the last parameter in one dimension is used to verify the estimated parameters by histogram. The parameters are determined coarsely and quantization accuracy is increased relatively by a multiresolution strategy. Experimental results in different road scene and a comparison with other methods have proven the validity of the proposed method.