Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations
| Autor: | Elena Toth, Stefano Mattoccia, Fabio Tosi, Flavia Tauro, R. Piscopia, Salvatore Grimaldi |
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| Přispěvatelé: | Tauro, Flavia, Tosi, Fabio, Mattoccia, Stefano, Toth, Elena, Piscopia, Rodolfo, Grimaldi, Salvatore |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
large scale particle image velocimetry
Computer science Science 0208 environmental biotechnology Optical flow ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Field of view 02 engineering and technology optical flow optical tracking velocimetry (OTV) streamflow Lucas-Kanade FAST feature detection feature tracking particle tracking velocimetry gauge-cam Lucas–Kanade method Particle tracking velocimetry Computer vision Image resolution Orientation (computer vision) business.industry Velocimetry 020801 environmental engineering General Earth and Planetary Sciences Artificial intelligence Noise (video) business |
| Zdroj: | Remote Sensing; Volume 10; Issue 12; Pages: 2010 Remote Sensing, Vol 10, Iss 12, p 2010 (2018) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs10122010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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