Google Research

Unifying Data for Fine-Grained Species Classification

(2019) (to appear)

Abstract

Wildlife monitoring is an essential part of nature conservation. Traditionally this has been done by human experts working in the field limiting the reach and extent. With the advances in AI and computer vision over the past decade, it can be put to an effective use for this purpose. There is gigantic amount of data collected from camera trap images over the years, tagged by experts. It can be used to build models to automatically tag new images efficiently. We outline the data collection effort from various conservation partners to unify the same for training AI-based classification models using deep neural networks. Evaluation of widely-used models trained on 2.9M images across 465 fine grained species enable us to achieve an accuracy of 83.92\%. We then outline work in progress to improve these models by addressing the common challenges and how we can effectively use inherent characteristics of camera trap data thus reducing the load on human experts and contributing to nature conservation effort by effectively monitoring the wildlife.

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