The iWildCam 2018 Challenge Dataset
This work provides a dataset for developing automated annotation systems to help ecologists and researchers scale biodiversity studies, but it is incremental as it focuses on dataset creation rather than a new method.
The paper tackles the problem of scaling biodiversity research by addressing the bottleneck of human annotation for camera trap data, and introduces a challenge dataset to test the generalization of deep learning solutions to novel locations.
Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most useful.