Recognition in Terra Incognita
This addresses the challenge of developing robust detection and classification systems for real-world applications like wildlife monitoring, but it is incremental as it focuses on benchmarking rather than solving the generalization issue.
The paper tackles the problem of generalization in recognition algorithms to new environments by introducing a dataset from camera traps, finding that while state-of-the-art methods perform well in familiar locations, they show poor generalization to unseen locations, particularly for classification.
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.