基于平行Snake耦合Kalman滤波器的车道线检测算法

针对当前车道线检测算法中易受到车道线磨损、遮挡、阴影等影响,导致检测算法精度不高,鲁棒性不强,提出了平行Snake耦合Kalman滤波器的车道线检测方案。首先,为了获得道路左右边界的平行属性,引入期望最大化(EM)算子,通过最小化目标函数来估计消失点,并估算其单应矩阵;并在齐次坐标空间中进行单应性变换,将车道线透视图转变为鸟瞰图。然后,通过参数预测算子建立车道模型,将平行性约束添加到主动轮廓模型(Snake)中,构建了一种平行Snake车道线检测方法。在平行Snake方法中,为了克服图像梯度低时Snake无法有效收敛到车道边界,引入了膨胀力,将两条平行的主动轮廓往道路的左右两边推挤,最终收敛到道路的左右边沿。最后,考虑到前后帧之间参数的连续性,采用Kalman滤波器进行跟踪优化,并抑制噪声,提高算法对车道线的识别精度。实验结果表明,与当前常用的车道线检测算法比较,提出的方案在精度与鲁棒性均得到改善,在阴影、光照变化、边界破损等车道数据集上取得了良好的性能。

Lane line detection algorithm based on parallel Snake coupled Kalman filter

Current lane detection algorithm is easily affected by lane line wear, occlusion, shadow and so on, which results in low accuracy and robustness of the detection algorithm, a lane detection scheme for parallel Snake coupled with Kalman filter was proposed. Firstly, in order to obtain the parallel properties of left and right boundaries of road, expectation maximization operator was introduced, and the vanishing point was estimated by minimizing the objective function to estimate its homography matrix. In the homogeneous coordinate space, the homography transformation was used to complete the change of the lane line perspective to the bird''s eye view. Then, a lane model was established by parameter prediction operator. A parallel Snake lane detection method was constructed by adding parallelism constraints to active contour model, eventually converge on the left and right edges of the road. Finally, taking into account the continuity of the parameters between the front and back frames, the Kalman filter was used to track and optimize, and the noise was suppressed to improve the recognition accuracy of the lane line. Experimental results show that: compared with the commonly used lane detection algorithms, the proposed scheme has improved accuracy and robustness, and achieves good performance in lane datasets such as shadow, illumination change and boundary damage.