mlsanityData to 7 Oct 2026

SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity

arXiv 2209.09070 ↗First posted 19 Sep 2022cs.CV cs.LGcited by 10

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Abstract (arXiv)

The development and application of modern technology is an essential basis for the efficient monitoring of species in natural habitats and landscapes to trace the development of ecosystems, species communities, and populations, and to analyze reasons of changes. For estimating animal abundance using methods such as camera trap distance sampling, spatial information of natural habitats in terms of 3D (three-dimensional) measurements is crucial. Additionally, 3D information improves the accuracy of animal detection using camera trapping. This study presents a novel approach to 3D camera trapping featuring highly optimized hardware and software. This approach employs stereo vision to infer 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES). A comprehensive evaluation of SOCRATES shows not only a 3.23% improvement in animal detection (bounding box mAP75) but also its superior applicability for estimating animal abundance using camera trap distance sampling. The software and documentation of SOCRATES is provided at https://github.com/timmh/socrates

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 29 of its 72 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

29 of 72 references.

References · 29

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17 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 3; 2026: 1.

10 papers.

  1. 2023Segment Anythingcited by 8,158
  2. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  3. 2025DICEPTION: A Generalist Diffusion Model for Visual Perceptual Taskscited by 37
  4. 2023What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentationcited by 24
  5. 2024ZIM: Zero-Shot Image Matting for Anythingcited by 15
  6. 2025SAM2 for Image and Video Segmentation: A Comprehensive Surveycited by 13
  7. 2024ASAM: Boosting Segment Anything Model with Adversarial Tuningcited by 12
  8. 2024Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objectscited by 5
  9. 2025M2N2V2: Multi-Modal Unsupervised and Training-free Interactive Segmentationcited by 1
  10. 2026Segment Anything with Robust Uncertainty-Accuracy Correlationcited by 0