  Photo-trapping cameras are widely employed for wildlife monitoring. Those
cameras take photographs when motion is detected to capture images where
animals appear. A significant portion of these images are empty - no wildlife
appears in the image. Filtering out those images is not a trivial task since it
requires hours of manual work from biologists. Therefore, there is a notable
interest in automating this task. Automatic discarding of empty photo-trapping
images is still an open field in the area of Machine Learning. Existing
solutions often rely on state-of-the-art supervised convolutional neural
networks that require the annotation of the images in the training phase.
PARDINUS (Weakly suPervised discARDINg of photo-trapping empty images based on
aUtoencoderS) is constructed on the foundation of weakly supervised learning
and proves that this approach equals or even surpasses other fully supervised
methods that require further labeling work.
