Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps

TL;DR

This paper introduces an automated pipeline that significantly reduces manual effort in estimating animal distances in camera trap images, improving the accuracy and efficiency of abundance estimation crucial for biodiversity conservation.

Contribution

The study presents a novel automated method using monocular depth estimation to replace manual distance measurements in animal abundance surveys.

Findings

Manual effort reduced by over 21 times

Automated system improves distance estimation accuracy

System available at https://timm.haucke.xyz/publications/distance-estimation-animal-abundance

Abstract

The biodiversity crisis is still accelerating, despite increasing efforts by the international community. Estimating animal abundance is of critical importance to assess, for example, the consequences of land-use change and invasive species on community composition, or the effectiveness of conservation interventions. Various approaches have been developed to estimate abundance of unmarked animal populations. Whereas these approaches differ in methodological details, they all require the estimation of the effective area surveyed in front of a camera trap. Until now camera-to-animal distance measurements are derived by laborious, manual and subjective estimation methods. To overcome this distance estimation bottleneck, this study proposes an automatized pipeline utilizing monocular depth estimation and depth image calibration methods. We are able to reduce the manual effort required by a factor greater than 21 and provide our system at https://timm.haucke.xyz/publications/distance-estimation-animal-abundance