Efficient Pipeline for Camera Trap Image Review

TL;DR

This paper introduces an efficient pipeline that combines a pre-trained animal detector with region-specific training to improve species classification accuracy in camera trap images across different geographic areas.

Contribution

It presents a novel pipeline leveraging pre-trained detectors and minimal labeled data to adapt species classification models to new regions.

Findings

High classification accuracy achieved in new regions

Reduced need for extensive labeled datasets

Effective transferability across geographic areas

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.