  It is desirable for detection and classification algorithms to generalize to
unfamiliar environments, but suitable benchmarks for quantitatively studying
this phenomenon are not yet available. We present a dataset designed to measure
recognition generalization to novel environments. The images in our dataset are
harvested from twenty camera traps deployed to monitor animal populations.
Camera traps are fixed at one location, hence the background changes little
across images; capture is triggered automatically, hence there is no human
bias. The challenge is learning recognition in a handful of locations, and
generalizing animal detection and classification to new locations where no
training data is available. In our experiments state-of-the-art algorithms show
excellent performance when tested at the same location where they were trained.
However, we find that generalization to new locations is poor, especially for
classification systems.
