Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps

Haucke, Timm; Kühl, Hjalmar S.; Hoyer, Jacqueline; Steinhage, Volker

doi:10.1016/j.ecoinf.2021.101536

Cited by 17 publications

(31 citation statements)

References 10 publications

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“…Due to the time savings, the complete automation of the process enables the possibility of large-scale animal abundance studies and could accelerate biodiversity research. Although a direct comparison is difficult due to the different generation settings of the used datasets, the MAE per instance of 0.9864 m on single detections (Section 2.5) compared to MAE of 1.85 m of the interactive approach of (Haucke et al, 2022) may give an indication of the accuracy of the presented approach.…”

Section: Discussionmentioning

confidence: 99%

“…So far, three different approaches are available to derive such camera-animal distances. However, they rely either on the manual and laborious evaluation of reference images (Howe et al, 2017), even more time-consuming on-site distance measurements (Rowcliffe et al, 2011) or semi-automatic calibration of relative depth images for the specific sequence (Haucke et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

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Automated Distance Estimation for Wildlife Camera Trapping

Peter¹,

Haucke²,

Steinhage³

2022

Preprint

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The ongoing biodiversity crysis calls for accurate estimation of animal density and abundance to identify, for example, 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 animal 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. In this study, we propose a fully automatic approach to estimate camera-to-animal distances, based on monocular depth estimation (MDE), and without the need of reference image material. We leverage stateof-the-art relative MDE and a novel alignment procedure to estimate metric distances. We evaluate the approach on a zoo scenario dataset unseen during training. We achieve a mean absolute distance estimation error of only 0.9864 meters at a precision of 90.3% and recall of 63.8%, while completely eliminating the previously required manual effort for biodiversity researchers. The code will be made available.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automated Distance Estimation for Wildlife Camera Trapping

Peter¹,

Haucke²,

Steinhage³

2022

Preprint

Self Cite

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“…The effective area is statistically inferred by using the distances of the observed animals. Although there are approaches that estimate these distances (semi) automatically (Haucke et al, 2022;Johanns et al, 2022), they either require laborious capture of reference material (Haucke et al, 2022) or might not generalize to extreme scenarios such as very close-up scenes (Johanns et al, 2022;Auda, 2022).…”

Section: Abundance Estimationmentioning

confidence: 99%

“…it facilitates the verification of monocular approaches. For example, abundance estimation using camera trap distance sampling might be performed twice, once using monocular approaches (Haucke et al, 2022;Johanns et al, 2022) and once using SOCRATES. Both raw animal distances and the resulting animal densities might then be compared.…”

Section: Socratesmentioning

confidence: 99%

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SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity

Haucke¹,

Kühl²,

Steinhage³

2022

Preprint

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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 mAP 75 ) 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.

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Efficient data collection for camera trap‐based density estimation: A preliminary assessment

Ternyik,

McKaughan,

Hill

et al. 2024

Ecol Sol and Evidence

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Camera traps have great potential for generating wildlife insights by providing high resolution site‐specific data. Methods of data collection and analysis reliant on these tools for population density estimation can be relatively resource intensive, hindering their mainstream adoption. Here, we explore the potential of population density estimates derived from a distance sampling method based on optics theory, which greatly simplifies the process of setting up camera sites and analysing data. Specifically, we (1) tested the method on human subjects in an artificial environment, (2) compared it to another method relying on virtual grids on images using wild populations of black‐backed jackal (Canis mesomelas) and African civet (Civettictis civetta) in South Africa and (3) deployed it to estimate wild boar (Sus scrofa) population densities in Hungary. The initial human trials resulted in an estimate that was extremely close to true population density. When compared to the virtual grid method, results suggest that our distance sampling method can deliver accurate estimates with increased convenience and robustness against disruptions of the camera sites. The wild boar study resulted in a realistic density estimate, which can be used as a baseline when assessing future fluctuations in population density. As this new approach does not have special requirements for setting up camera sites, it is efficient and widely applicable across other density estimation methods requiring an estimate for effective detection distance. Additionally, the method can be applied in the retrospective analysis of existing datasets.

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Overcoming the distance estimation bottleneck in estimating animal abundance with camera traps

Cited by 17 publications

References 10 publications

Automated Distance Estimation for Wildlife Camera Trapping

Automated Distance Estimation for Wildlife Camera Trapping

SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity

Efficient data collection for camera trap‐based density estimation: A preliminary assessment

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