# Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data

arXiv: https://arxiv.org/abs/1811.07749 · HTML: https://mlsanity.com/paper/1811.07749/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 30 of 46)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [The iWildCam 2021 Competition Dataset](https://mlsanity.com/paper/2105.03494/) (2021)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals](https://mlsanity.com/paper/2406.09211/) (2024)
- [Automated wildlife image classification: An active learning tool for ecological applications](https://mlsanity.com/paper/2303.15823/) (2023)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [Florida Wildlife Camera Trap Dataset](https://mlsanity.com/paper/2106.12628/) (2021)
- [Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary](https://mlsanity.com/paper/2212.00266/) (2022)
- [Human-in-the-Loop Visual Re-ID for Population Size Estimation](https://mlsanity.com/paper/2312.05287/) (2023)
- [MetaWild: A Multimodal Dataset for Animal Re-Identification with Environmental Metadata](https://mlsanity.com/paper/2501.13368/) (2025)
- [Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook](https://mlsanity.com/paper/2109.14737/) (2021)
- [NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation](https://mlsanity.com/paper/2206.02498/) (2022)
- [CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx](https://mlsanity.com/paper/2506.04931/) (2025)
- [An Individual Identity-Driven Framework for Animal Re-Identification](https://mlsanity.com/paper/2410.22927/) (2024)
- [Pairwise Matching of Intermediate Representations for Fine-grained Explainability](https://mlsanity.com/paper/2503.22881/) (2025)
- [Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?](https://mlsanity.com/paper/2111.12805/) (2021)
- [MOTCOM: The Multi-Object Tracking Dataset Complexity Metric](https://mlsanity.com/paper/2207.10031/) (2022)
- [Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data](https://mlsanity.com/paper/2304.09657/) (2023)
- [Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling](https://mlsanity.com/paper/2511.06658/) (2025)
- [Finer-Personalization Rank: Fine-Grained Retrieval Examines Identity Preservation for Personalized Generation](https://mlsanity.com/paper/2512.19026/) (2025)
- [Image-to-Image Translation of Synthetic Samples for Rare Classes](https://mlsanity.com/paper/2106.12212/) (2021)
- [A contrastive learning approach for individual re-identification in a wild fish population](https://mlsanity.com/paper/2301.00596/) (2023)
- [Membership Inference Attack for Beluga Whales Discrimination](https://mlsanity.com/paper/2302.14769/) (2023)
- [Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats](https://mlsanity.com/paper/2406.13002/) (2024)
- [CNN Based Flank Predictor for Quadruped Animal Species](https://mlsanity.com/paper/2406.13588/) (2024)
- [Metadata augmented deep neural networks for wild animal classification](https://mlsanity.com/paper/2409.04825/) (2024)
- [Adapting the re-ID challenge for static sensors](https://mlsanity.com/paper/2412.00290/) (2024)

## Abstract (arXiv)

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. 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. 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 re-identify 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.