Zhao Donghua, Wu Yicheng, Wang Chenguang, Shen Chong, Tang Jun, Liu Jun, Yu Hua, Lu Zhumao
Appl Opt. 2021 Dec 1;60(34):10600-10609. doi: 10.1364/AO.441233.
The optical flow method has been widely used to measure the vehicle velocity by observing the stationary ground with a camera looking-down. However, when there are moving objects on the stationary ground, the interfering optical flow field will be generated, which decreases the velocity measurement accuracy of a vehicle relative to the ground. In order to reduce the effects caused by moving objects, this paper integrates pyramid Lucas-Kanade (LK) algorithm with the gray consistency method to use the information of color images thoroughly. First, a mask region with convolutional neural network (Mask-R-CNN) is used to recognize the objects that have motions relative to the ground, and it covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then images are decomposed into three channels, red, green, and blue (i.e., , , and ), and processed by median filter. Based on the gray consistency method, the optical flow can be obtained by the pyramid LK algorithm. Finally, the velocity is calculated by the optical flow value. The prominent advantages of the proposed algorithm are: (i) increase the velocity measurement accuracy of a vehicle relative to the ground; (ii) use the information of color images acquired with cameras thoroughly and obtain velocity calculation outputs with less fluctuation; (iii) reduce wrong values caused by noises that are from the origin image and introduced by similar color masks. Four experiments are conducted to test the proposed algorithm and results with superior precision and reliability show the feasibility and effectiveness of the proposed method for the velocity measurement accuracy of a vehicle relative to the ground.
光流法已被广泛用于通过向下看的相机观察静止地面来测量车辆速度。然而，当静止地面上存在移动物体时，会产生干扰光流场，这会降低车辆相对于地面的速度测量精度。为了减少移动物体造成的影响，本文将金字塔卢卡斯 - 卡纳德（LK）算法与灰度一致性方法相结合，以充分利用彩色图像的信息。首先，使用带卷积神经网络的掩码区域（Mask-R-CNN）识别相对于地面有运动的物体，并用掩码覆盖它们，以增强像素之间的相似性并减少噪声移动像素的影响。然后将图像分解为红、绿、蓝三个通道（即 、 和 ），并通过中值滤波器进行处理。基于灰度一致性方法，可通过金字塔LK算法获得光流。最后，根据光流值计算速度。该算法的突出优点是：（i）提高车辆相对于地面的速度测量精度；（ii）充分利用相机采集的彩色图像信息，获得波动较小的速度计算输出；（iii）减少由原始图像噪声和相似颜色掩码引入的错误值。进行了四项实验来测试该算法，具有卓越精度和可靠性的结果表明了该方法对于车辆相对于地面速度测量精度的可行性和有效性。
