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首页> 外文会议>Hydrological Sciences and Water Security: Past, Present and Future >River surface flow estimation using a camera: a case study on the Tiber River
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River surface flow estimation using a camera: a case study on the Tiber River

机译：用相机估算河面流量：以台伯河为例

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摘要

Monitoring surface water velocity during flood events is a challenging task. Techniques based on deploying instruments in the flow are often not feasible due to high velocity and abundant sediment transport. As a consequence, flow measurement campaigns are infrequent and stream velocity observations during major floods are scarce or absent. In the last few years, large-scale particle image velocimetry (LSPIV) was proposed to overcome some of these drawbacks (Fujita et al. 1997). The novelty of LSPIV should be sought in its capacity of extracting desired kinematic information from a video of the surface streamflow. The general implementation of LSPIV can be summarized in three main sequential steps: video recording, image pre-processing, and image analysis. The video recording is the simplest phase, as it can be executed with a low-cost sport camera at a standard sampling frequency (30-60 frames per second - fps) and at full-HD image resolution (1920 × 1080 pixels). An image pre-processing step is necessary for treating video frames before the image analysis. Typical pre-processing includes pixel calibration, frame correction and frame matching, to compensate for distortions and undesired mechanical vibrations. The treated frame sequence is ultimately processed through standard PIV algorithms that return the sought velocity maps. PIV (Adrian 1991) is based on the cross-correlation of pairs of consecutive frames, in which each frame is subdivided into interrogation windows that are translated on a pixel grid.
机译：在洪水事件期间监视地表水流速是一项艰巨的任务。由于流速高和泥沙输送量大，在流动中使用基于仪器的技术通常不可行。结果，很少进行流量测量活动，并且在大洪水期间很少或没有观测到水流速度。在最近几年中，提出了大规模粒子图像测速技术（LSPIV）来克服这些缺点中的一些缺点（Fujita等，1997）。 LSPIV的新颖之处在于它可以从表面流视频中提取所需的运动学信息。 LSPIV的一般实现可以概括为三个主要的顺序步骤：视频记录，图像预处理和图像分析。视频记录是最简单的阶段，因为它可以用低成本运动相机以标准采样频率（每秒30-60帧-fps）和全高清图像分辨率（1920×1080像素）执行。对于图像分析之前的视频帧，必须执行图像预处理步骤。典型的预处理包括像素校准，帧校正和帧匹配，以补偿失真和不希望的机械振动。经过处理的帧序列最终通过返回所需速度图的标准PIV算法进行处理。 PIV（Adrian 1991）基于连续帧对之间的互相关，其中每个帧都细分为在像素网格上转换的询问窗口。

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    F. TAURO; M. PORFIRI; A. PETROSELLI; G. OLIVIERI; R. RAPITI; G. CIPOLLARI; S. GRIMALDI;

  • 作者单位

    Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA;

    Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA;

    Department of Agriculture, Forests, Nature and Energy (DAFNE), University of Tuscia, Via San Camillo De Lellis snc, 01100 Viterbo, Italy;

    Department of Agriculture, Forests, Nature and Energy (DAFNE), University of Tuscia, Via San Camillo De Lellis snc, 01100 Viterbo, Italy;

    Department of Agriculture, Forests, Nature and Energy (DAFNE), University of Tuscia, Via San Camillo De Lellis snc, 01100 Viterbo, Italy;

    Department of Agriculture, Forests, Nature and Energy (DAFNE), University of Tuscia, Via San Camillo De Lellis snc, 01100 Viterbo, Italy;

    Department of Mechanical and Aerospace Engineering, Polytechnic School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA,Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), University of Tuscia, Via San Camillo De Lellis snc, 01100 Viterbo, Italy;

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