Past, present and future approaches using computer vision for animal re‐identification from camera trap data
Open accessAbstract
The ability of a researcher to re‐identify (re‐ ID ) an individual animal upon re‐encounter is fundamental for addressing a broad range of questions in the study of ecosystem function, community and population dynamics and behavioural ecology. Tagging animals during mark and recapture studies is the most common method for reliable animal re‐ ID ; however, camera traps are a desirable alternative, requiring less labour, much less intrusion and prolonged and continuous monitoring into an environment. Despite these advantages, the analyses of camera traps and video for re‐ ID by humans are criticized for their biases related to human judgement and inconsistencies between analyses. In this review, we describe a brief history of camera traps for re‐ ID , present a collection of computer vision feature engineering methodologies previously used for animal re‐ ID , provide an introduction to the underlying mechanisms of deep learning relevant to animal re‐ ID , highlight the success of deep learning methods for human re‐ ID , describe the few ecological studies currently utilizing deep learning for camera trap analyses and our predictions for near future methodologies based on the rapid development of deep learning methods. For decades, ecologists with expertise in computer vision have successfully utilized feature engineering to extract meaningful features from camera trap images to improve the statistical rigor of individual comparisons and remove human bias from their camera trap analyses. Recent years have witnessed the emergence of deep learning systems which have demonstrated the accurate re‐ ID of humans based on image and video data with near perfect accuracy. Despite this success, ecologists have yet to utilize these approaches for animal re‐ ID . By utilizing novel deep learning methods for object detection and similarity comparisons, ecologists can extract animals from an image/video data and train deep learning classifiers to re‐ ID animal individuals beyond the capabilities of a human observer. This methodology will allow ecologists with camera/video trap data to reidentify individuals that exit and re‐enter the camera frame. Our expectation is that this is just the beginning of a major trend that could stand to revolutionize the analysis of camera trap data and, ultimately, our approach to animal ecology.
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The standard for identification of animal species and identity is DNA profiling, which can be difficult to scale to large, distributed populations.
“The standard for identification of animal species and identity is DNA profiling97, which can be difficult to scale to large, distributed populations54,93.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [54]
Conventional and more recently DL-based methods have reached strong performance for specific taxa, especially across small populations.
“Yet, conventional101 and more recently DL-based38,54,102 methods have reached strong performance for specific taxa, especially across small populations.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [54]
Computer vision recently emerged as a powerful alternative for automatic individual identification.
“Similar to counting and reconstruction (see next section), computer vision recently emerged as a powerful alternative for automatic individual identification54,98–100.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [54]
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- Authors
- Schneider, S., Taylor, G. W., Linquist, S., & Kremer, S. C.
- Affiliations
- University of Guelph (CA)
- Corresponding author
- Schneider, Stefan
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2019-01-20
- Volume
- 10
- Issue
- 4
- Pages
- 461-470
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2901771398
- Cited by
- 245
- References
- 53
- Citations by year
- 2026: 35 · 2025: 44 · 2024: 35 · 2023: 35 · 2022: 37 · 2021: 33 · 2020: 23 · 2019: 3
- FWCI
- 11.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Methods in Ecology and Evolution (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- animal re-identification, camera trap data, computer vision, deep learning, feature engineering, mark-recapture, object detection, similarity analysis, person re-identification, animal ecology
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