# ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification

arXiv: https://arxiv.org/abs/2106.15083 · HTML: https://mlsanity.com/paper/2106.15083/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 77 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [The iWildCam 2020 Competition Dataset](https://mlsanity.com/paper/2004.10340/) (2020)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [A deep active learning system for species identification and counting in camera trap images](https://mlsanity.com/paper/1910.09716/) (2019)
- [Synthetic Examples Improve Generalization for Rare Classes](https://mlsanity.com/paper/1904.05916/) (2019)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017)
- [Face Recognition: Primates in the Wild](https://mlsanity.com/paper/1804.08790/) (2018)
- [Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer](https://mlsanity.com/paper/1902.09324/) (2019)
- [Deep Learning Object Detection Methods for Ecological Camera Trap Data](https://mlsanity.com/paper/1803.10842/) (2018)
- [The iWildCam 2018 Challenge Dataset](https://mlsanity.com/paper/1904.05986/) (2019)
- [Automated Visual Fin Identification of Individual Great White Sharks](https://mlsanity.com/paper/1609.06323/) (2016)
- [Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales](https://mlsanity.com/paper/1708.07785/) (2017)
- [Towards Automatic Identification of Elephants in the Wild](https://mlsanity.com/paper/1812.04418/) (2018)
- [The iWildCam 2019 Challenge Dataset](https://mlsanity.com/paper/1907.07617/) (2019)
- [Distinguishing Individual Red Pandas from Their Faces](https://mlsanity.com/paper/1908.03391/) (2019)
- [Automatic Detection and Recognition of Individuals in Patterned Species](https://mlsanity.com/paper/2005.02905/) (2020)

## Built on by (most cited first, 8 of 8)

- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [Unsupervised Pelage Pattern Unwrapping for Animal Re-identification](https://mlsanity.com/paper/2506.15369/) (2025)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [Membership Inference Attack for Beluga Whales Discrimination](https://mlsanity.com/paper/2302.14769/) (2023)
- [Elephants and Algorithms: A Review of the Current and Future Role of AI in Elephant Monitoring](https://mlsanity.com/paper/2306.13803/) (2023)
- [Are They the Same Picture? Adapting Concept Bottleneck Models for Human-AI Collaboration in Image Retrieval](https://mlsanity.com/paper/2407.08908/) (2024)
- [Adapting the re-ID challenge for static sensors](https://mlsanity.com/paper/2412.00290/) (2024)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

African elephants are vital to their ecosystems, but their populations are threatened by a rise in human-elephant conflict and poaching. Monitoring population dynamics is essential in conservation efforts; however, tracking elephants is a difficult task, usually relying on the invasive and sometimes dangerous placement of GPS collars. Although there have been many recent successes in the use of computer vision techniques for automated identification of other species, identification of elephants is extremely difficult and typically requires expertise as well as familiarity with elephants in the population. We have built and deployed a web-based platform and database for human-in-the-loop re-identification of elephants combining manual attribute labeling and state-of-the-art computer vision algorithms, known as ElephantBook. Our system is currently in use at the Mara Elephant Project, helping monitor the protected and at-risk population of elephants in the Greater Maasai Mara ecosystem. ElephantBook makes elephant re-identification usable by non-experts and scalable for use by multiple conservation NGOs.