摘要
针对采用固定摄像的路况监视系统无法观看自如的缺点,提出了基于云台摄像的实时车速检测算法.建立了简化的摄像机参数模型,提取了线性拟合后的车道图像特征参数,并利用Kluge曲线模型和随机霍夫变换实现了像平面车道分割线的二维重建和云台摄像机的标定;应用自适应背景减除、扩展Kalman滤波器等方法,提取了帧运动域及域中目标轮廓,从而实现了车辆的精确定位、跟踪,以至实时速度检测.该算法已试用于工程实践,具有较好的鲁棒性.
A novel algorithm is presented to estimate real-time traffic speed by using images from uncalibrated PTZ cameras. Firstly, line parts of lane boundaries are obtained from the roadway-background images, and the camera is calibrated by estimating the slopes of such parts and their corresponding vanishing point. Then a generic lane boundaries detection via Kluge circular-model is carried out by Randomized Hough Transform. With the obtained lane boundaries, a vehicle tracker is established to track the moving vehicles between frames of the video sequences by a combination of the adaptive background subtraction and the extended Kalman filter technique. Finally, the vehicles＇ speed is estimated from images by transforming their image coordinates into the real-world coordinates by our simplified camera model. The experimental results show that, with great flexibility in camera calibration, our algorithm is both robust and efficient for the vehicle speed estimation.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2006年第9期1337-1344,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
中国交通部资助项目(200435333204)
江苏省交通厅科学研究项目(03x003)
关键词
云台摄像
图像特征参数
摄像机标定
实时车速检测
PTZ camera
image features
camera calibration
Real-time traffic speed estimation
