# Automated Distance Estimation for Wildlife Camera Trapping

arXiv: https://arxiv.org/abs/2202.04613 · HTML: https://mlsanity.com/paper/2202.04613/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [Learning to Recover 3D Scene Shape from a Single Image](https://mlsanity.com/paper/2012.09365/) (2020)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [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)
- [One Shot 3D Photography](https://mlsanity.com/paper/2008.12298/) (2020)
- [A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation](https://mlsanity.com/paper/1904.10230/) (2019)
- [Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sector](https://mlsanity.com/paper/1606.07781/) (2016)
- [Exploiting Depth Information for Wildlife Monitoring](https://mlsanity.com/paper/2102.05607/) (2021)
- [Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps](https://mlsanity.com/paper/2105.04244/) (2021)
- [Low-viewpoint forest depth dataset for sparse rover swarms](https://mlsanity.com/paper/2003.04359/) (2020)

## Built on by (most cited first, 5 of 5)

- [SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity](https://mlsanity.com/paper/2209.09070/) (2022)
- [Towards Application-Specific Evaluation of Vision Models: Case Studies in Ecology and Biology](https://mlsanity.com/paper/2505.02825/) (2025)
- [Benchmark on Monocular Metric Depth Estimation in Wildlife Setting](https://mlsanity.com/paper/2510.04723/) (2025)
- [Deep in the Jungle: Towards Automating Chimpanzee Population Estimation](https://mlsanity.com/paper/2601.22917/) (2026)
- [Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring](https://mlsanity.com/paper/2606.21613/) (2026)

## Abstract (arXiv)

The ongoing biodiversity crisis calls for accurate estimation of animal density and abundance to identify sources of biodiversity decline and effectiveness of conservation interventions. Camera traps together with abundance estimation methods are often employed for this purpose. The necessary distances between camera and observed animals are traditionally derived in a laborious, fully manual or semi-automatic process. Both approaches require reference image material, which is both difficult to acquire and not available for existing datasets. We propose a fully automatic approach we call AUtomated DIstance esTimation (AUDIT) to estimate camera-to-animal distances. We leverage existing state-of-the-art relative monocular depth estimation and combine it with a novel alignment procedure to estimate metric distances. AUDIT is fully automated and requires neither the comparison of observations in camera trap imagery with reference images nor capturing of reference image material at all. AUDIT therefore relieves biologists and ecologists from a significant workload. We evaluate AUDIT on a zoo scenario dataset unseen during training where we achieve a mean absolute distance estimation error over all animal instances of only 0.9864 meters and mean relative error (REL) of 0.113. The code and usage instructions are available at https://github.com/PJ-cs/DistanceEstimationTracking