mlsanityData to 14 Oct 2026

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

arXiv 2105.04244 ↗First posted 10 May 2021cs.CVcited by 4

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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

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

9 of 24 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  4. 2021Vision Transformers for Dense Predictioncited by 1,803
  5. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  6. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  7. 2019Efficient Pipeline for Camera Trap Image Reviewcited by 79
  8. 2019CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depthcited by 75
  9. 2021Exploiting Depth Information for Wildlife Monitoringcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

4 papers.