Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps

By Timm Haucke, Hjalmar S. Kühl, J. Hoyer, Volker Steinhage. 2021

28 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition, Ecological Modeling, Ecology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Peter Johanns
2. Timm Haucke
3. Volker Steinhage

In Ecological Modeling

1. Timm Haucke
2. Volker Steinhage
3. Peter Johanns

In Ecology

1. Timm Haucke
2. Volker Steinhage
3. Peter Johanns

Citations in the corpus

• Automated distance estimation for wildlife camera trapping (2022, methodology): “Although the process of manually comparing observation images with reference images has been automated, reference objects must still be located by hand (Haucke et al., 2022).”