# Efficient Pipeline for Camera Trap Image Review

arXiv: https://arxiv.org/abs/1907.06772 · HTML: https://mlsanity.com/paper/1907.06772/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 30 of 79)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Extending the WILDS Benchmark for Unsupervised Adaptation](https://mlsanity.com/paper/2112.05090/) (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)
- [Out-of-Domain Robustness via Targeted Augmentations](https://mlsanity.com/paper/2302.11861/) (2023)
- [PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition](https://mlsanity.com/paper/2401.13554/) (2024)
- [Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring](https://mlsanity.com/paper/2108.06435/) (2021)
- [SAOR: Single-View Articulated Object Reconstruction](https://mlsanity.com/paper/2303.13514/) (2023)
- [Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation](https://mlsanity.com/paper/2405.12930/) (2024)
- [MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps](https://mlsanity.com/paper/2503.18223/) (2025)
- [Automated wildlife image classification: An active learning tool for ecological applications](https://mlsanity.com/paper/2303.15823/) (2023)
- [Dynamic Curriculum Learning for Great Ape Detection in the Wild](https://mlsanity.com/paper/2205.00275/) (2022)
- [SOCRATES: A Stereo Camera Trap for Monitoring of Biodiversity](https://mlsanity.com/paper/2209.09070/) (2022)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [Controlled Training Data Generation with Diffusion Models](https://mlsanity.com/paper/2403.15309/) (2024)
- [Human-in-the-Loop Visual Re-ID for Population Size Estimation](https://mlsanity.com/paper/2312.05287/) (2023)
- [Insect Identification in the Wild: The AMI Dataset](https://mlsanity.com/paper/2406.12452/) (2024)
- [Animal Identification with Independent Foreground and Background Modeling](https://mlsanity.com/paper/2408.12930/) (2024)
- [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025)
- [Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence](https://mlsanity.com/paper/2202.02283/) (2022)
- [CATALOG: A Camera Trap Language-guided Contrastive Learning Model](https://mlsanity.com/paper/2412.10624/) (2024)
- [DataS^3: Dataset Subset Selection for Specialization](https://mlsanity.com/paper/2504.16277/) (2025)
- [Automated Distance Estimation for Wildlife Camera Trapping](https://mlsanity.com/paper/2202.04613/) (2022)
- [Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study](https://mlsanity.com/paper/2602.03894/) (2026)
- [Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps](https://mlsanity.com/paper/2105.04244/) (2021)
- [Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning](https://mlsanity.com/paper/2305.02097/) (2023)
- [CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx](https://mlsanity.com/paper/2506.04931/) (2025)
- [A first step towards automated species recognition from camera trap images of mammals using AI in a European temperate forest](https://mlsanity.com/paper/2103.11052/) (2021)
- [A benchmark for computational analysis of animal behavior, using animal-borne tags](https://mlsanity.com/paper/2305.10740/) (2023)
- [Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design](https://mlsanity.com/paper/2407.21028/) (2024)

## Abstract (arXiv)

Biologists all over the world use camera traps to monitor biodiversity and wildlife population density. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but it has proven difficult to to apply models trained in one region to images collected in different geographic areas. In some cases, accuracy falls off catastrophically in new region, due to both changes in background and the presence of previously-unseen species. We propose a pipeline that takes advantage of a pre-trained general animal detector and a smaller set of labeled images to train a classification model that can efficiently achieve accurate results in a new region.