TY - GEN
T1 - High speed lane recognition under complex road conditions
AU - Gong, Jianwei
AU - Wang, Anshuai
AU - Zhai, Yong
AU - Xiong, Guangming
AU - Zhou, Peiyun
AU - Chen, Huiyan
PY - 2008
Y1 - 2008
N2 - To improve the speed and stability of lanes recognition under complex road conditions, a rapid detection algorithm combined with dynamic window and prior knowledge is proposed. The method of Grid is used to segment initial image and define the region of interest (ROI), then all the pixels are eliminated except those on the intersection of the grid lines, thus feature pixels of the lane edge in these intersections will be detected and can be used to generate some dynamic windows with the dilatation algorithm. Then, images are processed in these dynamic windows to obtain lane edge feature. Slope and intercept of the lane can be attained by Hough transformation. To obtain the correct lane information, the information obtained in current processing cycle will be judged and filtered by the prior knowledge. Experiments in structured road showed that the speed of image processing was about 22ms/frame and the proposed algorithm could meet the real-time and stability requirements of high-speed vehicle vision navigation system.
AB - To improve the speed and stability of lanes recognition under complex road conditions, a rapid detection algorithm combined with dynamic window and prior knowledge is proposed. The method of Grid is used to segment initial image and define the region of interest (ROI), then all the pixels are eliminated except those on the intersection of the grid lines, thus feature pixels of the lane edge in these intersections will be detected and can be used to generate some dynamic windows with the dilatation algorithm. Then, images are processed in these dynamic windows to obtain lane edge feature. Slope and intercept of the lane can be attained by Hough transformation. To obtain the correct lane information, the information obtained in current processing cycle will be judged and filtered by the prior knowledge. Experiments in structured road showed that the speed of image processing was about 22ms/frame and the proposed algorithm could meet the real-time and stability requirements of high-speed vehicle vision navigation system.
UR - http://www.scopus.com/inward/record.url?scp=57749204371&partnerID=8YFLogxK
U2 - 10.1109/IVS.2008.4621226
DO - 10.1109/IVS.2008.4621226
M3 - Conference contribution
AN - SCOPUS:57749204371
SN - 9781424425693
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 566
EP - 570
BT - 2008 IEEE Intelligent Vehicles Symposium, IV
T2 - 2008 IEEE Intelligent Vehicles Symposium, IV
Y2 - 4 June 2008 through 6 June 2008
ER -