SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

By Timm Haucke, Hjalmar S. Kühl, Volker Steinhage. 2022

13 citations recorded by the source corpus.

The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, camera trapping in combination with three-dimensional (3D) measurements of habitats is highly valuable. Additionally, 3D information improves the accuracy of wildlife 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 the 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 openly provided.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, 3D Vision & Pose

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jitendra Malik
3. Xiaogang Wang
4. Dacheng Tao
5. Trevor Darrell
6. Alan L. Yuille
7. Ming-Hsuan Yang
8. Ross B. Girshick

In Environmental Engineering

1. Ross B. Girshick
2. Piotr Doll'ar
3. Alexander C. Berg

In 3D Vision & Pose

1. Nikhila Ravi
2. Ross B. Girshick
3. Piotr Doll'ar
4. Alexander C. Berg

Citations in the corpus

• Segment Anything (2023, background): “ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples”
• SAM 2: Segment Anything in Images and Videos (2024, unknown): “…2021a), NDD20 (Trotter et al., 2020), NDISPark (Ciampi et al., 2021, 2022), OVIS (Qi et al., 2022), PPDLS (Minervini et al., 2016), Plittersdorf (Haucke et al., 2022), STREETS (Snyder & Do, 2019), TimberSeg (Fortin et al., 2022), TrashCan (Hong et al., 2020), VISOR (Darkhalil et al., 2022;…”