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Photo-trapping cameras are widely employed for wildlife monitoring. Thosecameras take photographs when motion is detected to capture images whereanimals appear. A significant portion of these images are empty - no wildlifeappears in the image. Filtering out those images is not a trivial task since itrequires hours of manual work from biologists. Therefore, there is a notableinterest in automating this task. Automatic discarding of empty photo-trappingimages is still an open field in the area of Machine Learning. Existingsolutions often rely on state-of-the-art supervised convolutional neuralnetworks that require the annotation of the images in the training phase.PARDINUS (Weakly suPervised discARDINg of photo-trapping empty images based onaUtoencoderS) is constructed on the foundation of weakly supervised learningand proves that this approach equals or even surpasses other fully supervisedmethods that require further labeling work.

PARDINUS: Weakly supervised discarding of photo-trapping empty images based on autoencoders