Abstract. 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