Issue |
E3S Web Conf.
Volume 40, 2018
River Flow 2018 - Ninth International Conference on Fluvial Hydraulics
|
|
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Article Number | 03018 | |
Number of page(s) | 7 | |
Section | Hydraulic structures and their effects on bed, flow regime and ecology | |
DOI | https://doi.org/10.1051/e3sconf/20184003018 | |
Published online | 05 September 2018 |
Development and Tests of a 3D Fish-Tracking Videometry System for an Experimental Flume
1
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zürich, 8093 Zurich, Switzerland
2
German Federal Institute of Hydrology (BfG), 56002 Koblenz, Germany
3
Federal Waterways Engineering and Research Institute (BAW), 76187 Karlsruhe, Germany
* Corresponding author: detert@vaw.baug.ethz.ch
To design effective and efficient fish passage facilities at hydropower plants, the knowledge of swim behaviour of fish is essential. Therefore, living wild fish were investigated at different fish guidance structures in an experimental flume in a test section of 11 m length and 2.5 m width at water depths of about 0.6 m. Besides analysis of time data and manual recordings of the fish behaviour, video recordings of the fish movements can allow more detailed analysis of fish behaviour in different hydraulic situations. Thus, a videometry system was installed consisting of eleven synchronous cameras with overlapping views lined-up under dry conditions outside the flume. A 3D tracking algorithm was developed and implemented to analyse the video data. Core of the code is a motion-based multiple object tracking method, in which several objects can be tracked in 2D pixel-frame coordinates at the same time. After undistorting and stereo-calibrating the cameras, the 2D tracks are transferred to a 3D metric-space according to their epipolar geometry. Within this paper video data from a single experimental run of 15 min with three fishes with lengths of 100–150 mm are analysed exemplarily. The path-time diagram gives a distinct ‘big picture’ of the fish movement, which helps to identify preferred and disliked regions. However, due to imperfect actual camera setup, a 3D view in the near field of the cameras and an automated separation of individual tracks in a group of fish remains challenging.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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