Abstract
Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.
Highlights
Streamflow observations are of paramount importance in hydrological modelling and engineering practice [1,2,3]
optical tracking velocimetry (OTV) enabled successful extraction of the surface flow velocity of the Brenta River in moderate flow conditions. This is remarkable since experimental videos featured homogeneously and continuously seeded surfaces, which are inherently advantageous to correlation-based Large scale particle image velocimetry (LSPIV) and particle tracking velocimetry (PTV) rather than to optical flow [34]
The methodology was tested on two diverse image data sets: a set of videos collected on the Brenta River, where artificial seeding was provided, and the footage of a moderate flood captured by a gauge-cam on the Tiber River
Summary
Streamflow observations are of paramount importance in hydrological modelling and engineering practice [1,2,3]. Monitoring streamflow velocity facilitates estimation of river discharge and enables the comprehension of complex phenomena, such as erosion dynamics and sediment transport [4]. Flow velocity measurement relies on pointwise intrusive approaches, such as acoustic doppler current profilers and impeller flowmeters [5,6]. Standard remote methods include radars and ultrasonic flowmeters [7]. Most of these methods are expensive and some of them require time-consuming experimental campaigns and the presence of qualified personnel. Intrusive and highly user-assisted technology cannot be adopted to monitor abrupt phenomena, such as flash floods, and large flood events that may be risky for personnel and equipment [8]
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