# Exploiting Depth Information for Wildlife Monitoring

arXiv: https://arxiv.org/abs/2102.05607 · HTML: https://mlsanity.com/paper/2102.05607/

## Key facts

- Cited by 6 papers in the map (data through 7 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans](https://mlsanity.com/paper/1812.07003/) (2018)
- [Cross Modal Distillation for Supervision Transfer](https://mlsanity.com/paper/1507.00448/) (2015)
- [Deep Learning Object Detection Methods for Ecological Camera Trap Data](https://mlsanity.com/paper/1803.10842/) (2018)
- [Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling](https://mlsanity.com/paper/1604.05096/) (2016)
- [ClusterNet: 3D Instance Segmentation in RGB-D Images](https://mlsanity.com/paper/1807.08894/) (2018)

## Built on by (most cited first, 6 of 6)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [EdgeTAM: On-Device Track Anything Model](https://mlsanity.com/paper/2501.07256/) (2025)
- [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)
- [Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps](https://mlsanity.com/paper/2105.04244/) (2021)
- [Meerkat Behaviour Recognition Dataset](https://mlsanity.com/paper/2306.11326/) (2023)

## Abstract (arXiv)

Camera traps are a proven tool in biology and specifically biodiversity research. However, camera traps including depth estimation are not widely deployed, despite providing valuable context about the scene and facilitating the automation of previously laborious manual ecological methods. In this study, we propose an automated camera trap-based approach to detect and identify animals using depth estimation. To detect and identify individual animals, we propose a novel method D-Mask R-CNN for the so-called instance segmentation which is a deep learning-based technique to detect and delineate each distinct object of interest appearing in an image or a video clip. An experimental evaluation shows the benefit of the additional depth estimation in terms of improved average precision scores of the animal detection compared to the standard approach that relies just on the image information. This novel approach was also evaluated in terms of a proof-of-concept in a zoo scenario using an RGB-D camera trap.