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Open AccessFeature PaperArticle

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

1
Department for Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, 01100 Viterbo, Italy
2
Dipartimento di Informatica—Scienza e Ingegneria, University of Bologna, 40141 Bologna, Italy
3
Dipartimento di Ingegneria Civile, Chimica, Ambientale e dei Materiali, University of Bologna, 40141 Bologna, Italy
4
Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 10003, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 2010; https://doi.org/10.3390/rs10122010
Received: 25 October 2018 / Revised: 5 December 2018 / Accepted: 9 December 2018 / Published: 11 December 2018
(This article belongs to the Special Issue Remote Sensing of Surface Runoff)
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. View Full-Text
Keywords: optical tracking velocimetry (OTV); streamflow; optical flow; Lucas-Kanade; FAST; feature detection; feature tracking; particle tracking velocimetry; large scale particle image velocimetry; gauge-cam optical tracking velocimetry (OTV); streamflow; optical flow; Lucas-Kanade; FAST; feature detection; feature tracking; particle tracking velocimetry; large scale particle image velocimetry; gauge-cam
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MDPI and ACS Style

Tauro, F.; Tosi, F.; Mattoccia, S.; Toth, E.; Piscopia, R.; Grimaldi, S. Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations. Remote Sens. 2018, 10, 2010.

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