The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Rigoudy, Noa; Dussert, Gaspard; Benyoub, Abdelbaki; Besnard, Aurélien; Birck, Carole; Boyer, Jérome; Bollet, Yoann; Bunz, Yoann; Caussimont, Gérard; Chetouane, Elias; Chiffard, Jules; Cornette, Pierre; Delestrade, Anne; De Backer, Nina; Dispan, Lucie; Le Barh, Maden; Duhayer, Jeanne; Elder, Jean-François; Fanjul, Jean-Baptiste; Fonderflick, Jocelyn; Froustey, Nicolas; Garel, Mathieu; Gaudry, William; Gérard, Agathe; Giménez, Olivier; Hemery, Arzhela; Hemon, Audrey; Jullien, Jean‐Michel; Knitter, Daniel; Malafosse, Isabelle; Mărginean, Mircea; Ménard, Louise; Ouvrier, Alice; Pariset, Gwennaelle; Prunet, Vincent; Rabault, Julien; Randon, Malory; Raulet, Yann; Régnier, Antoine; Ribière, Romain; Ricci, Jean-Claude; Ruette, Sandrine; Schneylin, Yann; Sentilles, Jérôme; Siefert, Nathalie; R. Smith, Bethany; Terpereau, Guillaume; Touchet, Pierrick; Thuiller, Wilfried; Uzal, Antonio; Vautrain, Valentin; Vimal, Ruppert; Weber, Julian; Spataro, B.; Mièle, Vincent; Chamaillé‐Jammes, Simon

doi:10.1007/s10344-023-01742-7

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The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Published in European Journal of Wildlife Research • Oct 20, 2023

Authors:

Noa Rigoudy

Gaspard Dussert

Abdelbaki Benyoub

Abstract

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep learning classification models have allowed extraordinary progress towards th...

Subject

Wildlife

Camera trap

Computer science

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