TY - GEN
T1 - Versatile visual detection techniques for advanced safety vehicles
AU - Shen, Xin Liang
AU - Tseng, Din Chang
AU - Lin, Chun Wei
AU - Hu, Tony
AU - Liou, Regulus
PY - 2008
Y1 - 2008
N2 - A monocular visual detection system including twelve detection functions is proposed to assist the road driving for safety. In the lane-mark detection, the lateral inhibition property of human vision system is simulated to improve the detector to satisfy all different weather conditions and to avoid the influence of windshield wiper. The preceding vehicles are detected based on the underneath shadow, left/right borders, and multiple templates of vehicles; then verified by the ratio of lane and vehicle widths, symmetry, and gray-level variance of vehicle regions. The preceding-vehicle distance is estimated based on a single camera with only known focus length and setting height. The pitch and yaw angles of the camera are estimated from the proposed method. The experimental results show that the proposed methods are stable and effective for safety detection in various weather conditions: sunny, misty, dusty, cloudy, rainy day, and night. The average vehicle detected rate is 94.5 %.
AB - A monocular visual detection system including twelve detection functions is proposed to assist the road driving for safety. In the lane-mark detection, the lateral inhibition property of human vision system is simulated to improve the detector to satisfy all different weather conditions and to avoid the influence of windshield wiper. The preceding vehicles are detected based on the underneath shadow, left/right borders, and multiple templates of vehicles; then verified by the ratio of lane and vehicle widths, symmetry, and gray-level variance of vehicle regions. The preceding-vehicle distance is estimated based on a single camera with only known focus length and setting height. The pitch and yaw angles of the camera are estimated from the proposed method. The experimental results show that the proposed methods are stable and effective for safety detection in various weather conditions: sunny, misty, dusty, cloudy, rainy day, and night. The average vehicle detected rate is 94.5 %.
KW - Advance safety vehicle
KW - Computer vision
KW - Lane detection & classification
KW - Vehicle detection & distance estimation
UR - http://www.scopus.com/inward/record.url?scp=62749187750&partnerID=8YFLogxK
M3 - 會議論文篇章
AN - SCOPUS:62749187750
SN - 1601320787
SN - 9781601320780
T3 - Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
SP - 397
EP - 403
BT - Proceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
T2 - 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
Y2 - 14 July 2008 through 17 July 2008
ER -