Automated distance estimation for wildlife camera trapping

Peter, Johanns,; Haucke, Timm; Steinhage, Volker

doi:10.1016/j.ecoinf.2022.101734

Cited by 12 publications

(21 citation statements)

References 17 publications

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“…The effective area surveyed is statistically inferred by using the distances of the observed animals. Although there are approaches that estimate these distances (semi) automatically [ 25 , 26 ], they either require the laborious capture of reference material [ 25 ] or might not generalize to extreme scenarios, such as very close-up scenes within

of the camera [ 26 , 27 ].…”

Section: Related Workmentioning

confidence: 99%

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

Haucke

Kühl²,

Steinhage³

2022

Sensors

Self Cite

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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.

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of the camera [ 26 , 27 ].…”

Section: Related Workmentioning

confidence: 99%

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

Haucke

Kühl²,

Steinhage³

2022

Sensors

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%

“…In future studies, multiple SOCRATES installations should be used to estimate density at more sites and reduce the impact of spatially varying density. SOCRATES can also be paired cooperatively with traditional, monocular camera traps for improved error quantification and improvement of monocular distance estimations like that proposed by Johanns et al (2022).…”

Section: Abundance Estimation Using Socratesmentioning

confidence: 99%

“…Compared to competing approaches (Haucke et al, 2022;Johanns et al, 2022), distance estimation for abundance estimation of unmarked animal populations is straightforward with SOCRATES. Figure 9 visualizes the respective tradeoffs.…”

Section: Abundance Estimation Using Socratesmentioning

confidence: 99%

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SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity

Haucke¹,

Kühl²,

Steinhage³

2022

Preprint

Self Cite

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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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Large‐scale mammal monitoring: The potential of a citizen science camera‐trapping project in the United Kingdom

Hsing

Hill

Smith

et al. 2022

Ecol Sol and Evidence

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1. In light of global biodiversity loss, there is an increasing need for large-scale wildlife monitoring. This is difficult for mammals, since they can be elusive and nocturnal. In the United Kingdom, there is a lack of systematic, widespread mammal monitoring, and a recognized deficiency of data. Innovative new approaches are required.2. We developed MammalWeb, a portal to enable UK-wide camera trapping by a network of citizen scientists and partner organizations. MammalWeb citizen scientists contribute to both the collection and classification of camera trap data.Following trials in 2013-2017, MammalWeb has grown organically to increase its geographic reach (e.g. ∼2000 sites in Britain). It has so far provided the equivalent of over 340 camera trap-years of wild mammal monitoring, and produced nearly 440,000 classified image sequences and videos, of which, over 180,000 are mammal detections.3. We describe MammalWeb, its background, its development and the novel approaches we have for participation. We consider the data collected by Mammal-Web participants, especially in light of their relevance to the main goals of wildlife monitoring: to provide spatial data, abundance data and temporal behavioural data. MammalWeb can complement existing approaches to mammal monitoring. Explicit accounting for spatial and temporal patterns in animal activity enables accountingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Automated distance estimation for wildlife camera trapping

Cited by 12 publications

References 17 publications

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity

Large‐scale mammal monitoring: The potential of a citizen science camera‐trapping project in the United Kingdom

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