Abstract
Biodiversity measuring calls for massive capture of in situ fauna observations over a long period. The present research aims at reducing the cost and the bias of the measures by limiting human intervention for producing those observations. It is based on a integrated infrastructure made of an automated underwater camera trap and a collaborative visual interface to analyze, process and manage the observations. In a latter stage, the infrastructure will be complemented with full image processing and machine learning capabilities to automate as much as possible the handling and exploitation of the observations.
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References
Dornelas, M., et al.: Quantifying temporal change in biodiversity: challenges and opportunities. Proc. R. Soc. B: Biol. Sci. 280(1750), 20121931 (2012)
Drechsler, A., et al.: Ortmann’s funnel trap-a highly efficient tool for monitoring amphibian species. Herpetol. Notes 3, 13–21 (2010)
Mathe, M., et al.: Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies. Ecol. Evol. 7, 5861–5872 (2017)
Snoopy: Portable software for capture-recapture surveys. https://prezi.com/xqoogmni0ymu/snoopy
Dalal, N., Triggs, B. Histograms of oriented gradients for human detection. In: International Conference on Computer Vision and Pattern Recognition. IEEE Computer Society (2005)
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Didry, Y., Mestdagh, X., Tamisier, T. (2019). Newtrap: Improving Biodiversity Surveys by Enhanced Handling of Visual Observations. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2019. Lecture Notes in Computer Science(), vol 11792. Springer, Cham. https://doi.org/10.1007/978-3-030-30949-7_32
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DOI: https://doi.org/10.1007/978-3-030-30949-7_32
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