Accurate estimation of fish length in single camera photogrammetry with a fiducial marker. (14th March 2019)

• Record Type: Journal Article
• Authors: Monkman, Graham G; Hyder, Kieran; Kaiser, Michel J; Vidal, Franck P
• Editors: Beyan, Cigdem
• Is Part Of: ICES journal of marine science. Volume 77:Number 6(2020)
• Page Start: 2245
• Page End: 2254
• Publication Date: 2019-03-14
• Subjects: fiducial marker -- image distortion -- OpenCV -- photogrammetry -- single camera -- total length estimation; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz030
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems employed are complex, in contrast to "consumer" digital cameras and smartphones carried by potential citizen scientists. However, using consumer cameras in photogrammetry will introduce unknown length estimation errors through both the image acquisition process and lens distortion. This study presents a methodology to achieve accurate 2-dimensional (2-D) total length (TL) estimates of fish without specialist equipment or proprietary software. Photographs of fish were captured with an action camera using a background fiducial marker, a foreground fiducial marker and a laser marker. The geometric properties of the lens were modelled with OpenCV to correct image distortion. TL estimates were corrected for parallax effects using an algorithm requiring only the initial length estimate and known fish morphometric relationships. Correcting image distortion decreased RMSE by 96% and the percentage mean bias error (%MBE) by 50%. Correcting for parallax effects achieved a %MBE of −0.6%. This study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for complex camera equipment, making it particularly suitable for deployment in citizen science and other volunteer-based data collection endeavours.