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

arXiv: https://arxiv.org/abs/2105.04244 · HTML: https://mlsanity.com/paper/2105.04244/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth](https://mlsanity.com/paper/1904.02028/) (2019)
- [Exploiting Depth Information for Wildlife Monitoring](https://mlsanity.com/paper/2102.05607/) (2021)

## Built on by (most cited first, 4 of 4)

- [SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity](https://mlsanity.com/paper/2209.09070/) (2022)
- [Automated Distance Estimation for Wildlife Camera Trapping](https://mlsanity.com/paper/2202.04613/) (2022)
- [Towards Application-Specific Evaluation of Vision Models: Case Studies in Ecology and Biology](https://mlsanity.com/paper/2505.02825/) (2025)
- [Deep in the Jungle: Towards Automating Chimpanzee Population Estimation](https://mlsanity.com/paper/2601.22917/) (2026)

## Abstract (arXiv)

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