基于机器视觉的河水表面流速估计

为了解决河水流速的视频测量需要投掷浮标、效率低等问题,提出了基于机器视觉的河水表面流速测量方法。采用高清摄像机拍摄河水流动视频,捕捉河水流动时表面产生的波浪运动。为了凸显这些细微的水面运动,利用帧差法计算运动显著性图。提取相邻2帧显著性图的SURF特征点,通过特征点匹配法找出相邻2帧的匹配点,将匹配点间的距离作为特征点在2帧图像间的运动距离。计算了多帧图像间特征点运动距离的直方图,该直方图具有单峰特征;通过对直方图进行曲线拟合准确地找到峰值对应的距离,将其作为最优的运动距离。最后结合帧间时间和根据小孔成像原理导出的速度公式估计出河水表面流速。为了验证该方法的有效性,用流速仪和该方法进行了对比试验。结果表明,该方法具有精度好、稳定性高和运算速度快的优点。在低、中速河流速度估计时,该方法最大变异系数为1.63%,与流速仪测量结果的最大相对误差仅为3.12%。对2组数据的一致性分析表明,2组数据的皮尔逊相关系数和斯皮尔曼相关系数分别为0.998和0.990,显示了该方法的速度估计值与流速仪实测值有良好的一致性。与已有的图像处理方法相比,不仅更为准确,而且耗时更短。研究可为用其他机器视觉处理算法估计复杂水面和高速水流提供参考。

Estimation of surface speed of river flow based on machine vision

Abstract: The wave represents the motion of river flow. The surface speed of river can be estimated through motion analysis for the wave. In the paper, a method was proposed based on computer vision to estimate the surface speed of river directly. The method tried to capture the motion of wave caused by flowing river from the video. However, even taken by HD camera, the contrast between moving waves and even surface in an image is still not obvious since they are homogeneous and all moved as a whole. In order to enlarge details of the motion of waves, the map of motion saliency was calculated by the way of frame difference method. In the map, the key points were extracted and characterized by SURF features. These key points represented the most salient positions of waves. Through the point matching algorithm, a key point in one map and its counterparts in next map were searched. The correspondence between the 2 matched points indicated the motion of wave in the video and the distance between them was computed. In principle, with this distance and the parameters of camera, we could estimate the immediate speed of flow. However, the distance was noisy essentially. For robust and accurate estimation, we estimated the average speed instead of immediate speed. So, we calculated the histogram of the distances during the period of time. We found that most of these histograms appeared as uni-modal distribution. However, there existed some histograms which appeared with 2 adjacent peaks, or appeared with a flat peak. This resulted in the difficulty for estimation of distance accurately. To address the problem, we utilized the Gaussian curve to fit the histogram. The peak of the fitted curve could be searched accurately and its corresponding distance was viewed as the optimal estimation of average distance. Finally, with the speed formula derived from pinhole model, the optimal distance and the time between 2 maps, we could estimate the average surface speed of the river flow. To validate the availability of the proposed method, we compared the speeds estimated by our method with the baselines measured by the current meter. In our experimental setting, we selected gently surface for measurement task, without whirlpool and reflection. We conducted 8 measurements, with the speeds being limited between low and middle range. The experimental results showed that maximal relative error of speed between ours and the baseline was 3.12% while the min relative error only 1.39%, indicating good accuracy of our method. The min and max coefficient of variation was 1.04% and 1.63% respectively, showing high reliability. The correlation coefficients of Pearson and Spearman between our estimators and measured values were respectively 0.998 and 0.990. Bland-Altman regression P is 0.16, higher than 0.05 and in Bland-Altman scatter plot, most of points fell into the limits of agreement. These results showed that the flow speed estimated by our method had a good consistency with the baselines. In addition, our method was compared to the image processing method by previous literatures, the results showed that the time consumption was shortened by our study, which was only 4.4% of that of the literature, indicating that our method is faster than the previous method. In sum, this study provides an effective method for the estimation of flow speed of rivers with complex background.