River streamflow monitoring is currently facing a transformation due to the emerging of new innovative technologies. Fixed and mobile measuring systems are capable of quantifying surface flow velocities and discharges, relying on video acquisitions. This camera‐gauging framework is sensitive to what the camera can “observe” but also to field circumstances such as challenging weather conditions, river background transparency, transiting seeding characteristics, among others. This short communication paper introduces the novel idea of optimizing image velocimetry techniques selecting the most informative sequence of frames within the available video. The selection of the optimal frame window is based on two reasonable criteria: (a) the maximization of the number of frames, subject to (b) the minimization of the recently introduced dimensionless seeding distribution index (SDI). SDI combines seeding characteristics such as seeding density and spatial clustering of tracers, which are used as a proxy to enhance the reliability of image velocimetry techniques. Two field case studies were considered as a proof‐of‐concept of the proposed framework, on which seeding metrics were estimated and averaged in time to select the proper application window. The selected frames were analysed using LSPIV to estimate the surface flow velocities and river discharge. Results highlighted that the proposed framework might lead to a significant error reduction. In particular, the computed discharge errors, at the optimal portion of the footage, were about 0.40% and 0.12% for each case study, respectively. These values were lower than those obtained, considering all frames available.

中文翻译：

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改进用于流量监测的图像测速性能：播种指标以最大程度地减少错误

由于新的创新技术的出现，河流水流监测目前正面临转型。固定和移动测量系统能够依靠视频采集来量化地表流速和流量。该摄像机监控框架不仅对摄像机可以“观察”到的内容敏感，而且对诸如恶劣天气条件，河流背景透明性，过渡播种特性等野外条件也很敏感。这篇简短的交流论文介绍了优化图像测速技术的新思想，该技术选择了可用视频中帧信息最丰富的序列。最佳帧窗口的选择基于两个合理的标准：（a）最大化帧数，（b）尽量减少最近引入的无量纲播种分布指数（SDI）。SDI结合了诸如播种密度和示踪剂的空间聚类之类的播种特性，它们被用作代理来增强图像测速技术的可靠性。两个现场案例研究被认为是所提议框架的概念证明，在该框架上估计了播种指标并及时进行平均以选择合适的应用程序窗口。使用LSPIV对选定的框架进行分析，以估算地表流速和河流流量。结果强调，拟议的框架可能会导致显着减少错误。特别是，对于每个案例研究，在素材的最佳部分处，计算出的放电误差分别约为0.40％和0.12％。