Unifying data for fine-grained visual species classification
This work addresses the problem of efficient wildlife monitoring for conservationists, but it is incremental as it applies existing methods to new data.
The paper tackles the challenge of processing large volumes of wildlife camera trap images by presenting a deep convolutional neural network trained on 2.9M images across 465 species to automate species classification and reduce manual workload.
Wildlife monitoring is crucial to nature conservation and has been done by manual observations from motion-triggered camera traps deployed in the field. Widespread adoption of such in-situ sensors has resulted in unprecedented data volumes being collected over the last decade. A significant challenge exists to process and reliably identify what is in these images efficiently. Advances in computer vision are poised to provide effective solutions with custom AI models built to automatically identify images of interest and label the species in them. Here we outline the data unification effort for the Wildlife Insights platform from various conservation partners, and the challenges involved. Then we present an initial deep convolutional neural network model, trained on 2.9M images across 465 fine-grained species, with a goal to reduce the load on human experts to classify species in images manually. The long-term goal is to enable scientists to make conservation recommendations from near real-time analysis of species abundance and population health.