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

TL;DR

PARDINUS introduces a weakly supervised autoencoder-based method to automatically discard empty wildlife camera trap images, reducing manual effort and outperforming some fully supervised approaches.

Contribution

The paper presents a novel weakly supervised autoencoder approach for filtering empty images, eliminating the need for extensive manual annotations in wildlife monitoring.

Findings

Outperforms fully supervised methods in empty image detection

Reduces manual labeling effort significantly

Proves effectiveness of weakly supervised learning in this context

Abstract

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.