Efficient Pipeline for Camera Trap Image Review

TL;DR

The paper tackles the challenge of generalizing camera-trap species classification across regions by coupling a universal animal detector with region-specific classifiers trained on cropped animal images. This modular pipeline standardizes data ingestion, enables scalable multi-node detection, and simplifies classifier training, dramatically reducing manual review of empty images. A case study on Idaho Department of Fish and Game shows high detection precision across diverse conditions and substantial time savings, with initial classifier results remaining promising. The work provides practical tooling and a blueprint for deploying ML–driven camera-trap annotation in new projects.

Abstract

Biologists all over the world use camera traps to monitor biodiversity and wildlife population density. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but it has proven difficult to to apply models trained in one region to images collected in different geographic areas. In some cases, accuracy falls off catastrophically in new region, due to both changes in background and the presence of previously-unseen species. We propose a pipeline that takes advantage of a pre-trained general animal detector and a smaller set of labeled images to train a classification model that can efficiently achieve accurate results in a new region.