# A deep active learning system for species identification and counting in camera trap images

arXiv: https://arxiv.org/abs/1910.09716 · HTML: https://mlsanity.com/paper/1910.09716/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 30 of 44)

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- [Machine Learning and Deep Learning -- A review for Ecologists](https://mlsanity.com/paper/2204.05023/) (2022)
- [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)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [Insect Identification in the Wild: The AMI Dataset](https://mlsanity.com/paper/2406.12452/) (2024)
- [DataS^3: Dataset Subset Selection for Specialization](https://mlsanity.com/paper/2504.16277/) (2025)
- [Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study](https://mlsanity.com/paper/2602.03894/) (2026)
- [Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2401.00608/) (2023)
- [Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models](https://mlsanity.com/paper/2403.01101/) (2024)
- [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)
- [Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera-Trap Images](https://mlsanity.com/paper/2206.12458/) (2022)
- [Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps](https://mlsanity.com/paper/2304.12703/) (2023)
- [Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity](https://mlsanity.com/paper/2408.14348/) (2024)
- [Towards Zero-Shot Camera Trap Image Categorization](https://mlsanity.com/paper/2410.12769/) (2024)
- [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)
- [Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients](https://mlsanity.com/paper/2305.09856/) (2023)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [Long-tailed Species Recognition in the NACTI Wildlife Dataset](https://mlsanity.com/paper/2510.21657/) (2025)
- [Image-to-Image Translation of Synthetic Samples for Rare Classes](https://mlsanity.com/paper/2106.12212/) (2021)
- [ChatGPT in the context of precision agriculture data analytics](https://mlsanity.com/paper/2311.06390/) (2023)
- [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)
- [Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation](https://mlsanity.com/paper/2412.02262/) (2024)
- [A model-agnostic active learning approach for animal detection from camera traps](https://mlsanity.com/paper/2507.06537/) (2025)

## Abstract (arXiv)

Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and non-intrusive. However, extracting useful information from camera trap images is a cumbersome process: a typical camera trap survey may produce millions of images that require slow, expensive manual review. Consequently, critical information is often lost due to resource limitations, and critical conservation questions may be answered too slowly to support decision-making. Computer vision is poised to dramatically increase efficiency in image-based biodiversity surveys, and recent studies have harnessed deep learning techniques for automatic information extraction from camera trap images. However, the accuracy of results depends on the amount, quality, and diversity of the data available to train models, and the literature has focused on projects with millions of relevant, labeled training images. Many camera trap projects do not have a large set of labeled images and hence cannot benefit from existing machine learning techniques. Furthermore, even projects that do have labeled data from similar ecosystems have struggled to adopt deep learning methods because image classification models overfit to specific image backgrounds (i.e., camera locations). In this paper, we focus not on automating the labeling of camera trap images, but on accelerating this process. We combine the power of machine intelligence and human intelligence to build a scalable, fast, and accurate active learning system to minimize the manual work required to identify and count animals in camera trap images. Our proposed scheme can match the state of the art accuracy on a 3.2 million image dataset with as few as 14,100 manual labels, which means decreasing manual labeling effort by over 99.5%.