# Automating Visual Recognition of Leprosy in Wild Chimpanzees

arXiv: https://arxiv.org/abs/2607.17395 · HTML: https://mlsanity.com/paper/2607.17395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition](https://mlsanity.com/paper/2401.13554/) (2024)
- [Triple-stream Deep Metric Learning of Great Ape Behavioural Actions](https://mlsanity.com/paper/2301.02642/) (2023)
- [Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation](https://mlsanity.com/paper/2405.12930/) (2024)
- [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025)
- [The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition](https://mlsanity.com/paper/2502.21201/) (2025)
- [Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder](https://mlsanity.com/paper/2507.10552/) (2025)
- [GorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring](https://mlsanity.com/paper/2512.07776/) (2025)
- [PriVi: Towards A General-Purpose Video Model For Primate Behavior In The Wild](https://mlsanity.com/paper/2511.09675/) (2025)
- [Generating Synthetic Wildlife Health Data from Camera Trap Imagery: A Pipeline for Alopecia and Body Condition Training Data](https://mlsanity.com/paper/2603.26754/) (2026)

## Abstract (arXiv)

Leprosy (Mycobacterium leprae) has been confirmed in wild western chimpanzees (Pan troglodytes verus) in West Africa, presenting as clear and progressive visual symptoms. Manual review of camera-trap footage at landscape scale is infeasible, motivating the need for automated screening. We present the first deep learning pipeline for wildlife leprosy detection and contribute the PanLep300 dataset of 125,670 annotated bounding-box crops across 953 tracks from 303 camera-trap videos with ecologically-motivated splits that withhold whole individuals and camera installations. We benchmark spatial (2D), temporally aggregated (2.5D), and video-based (3D) classification approaches to investigate which approach is best suited to automated leprosy detection in wild apes. We find that simple aggregation of crop-level predictions consistently matches or outperforms both learned temporal models and end-to-end video architectures -- consistent with leprosy's static cutaneous presentation. We further find that performance is suppressed when tracklets contain frames of partially visible individuals -- as commonly occurs at the start and end of a track -- and demonstrate that this can be addressed through targeted construction and aggregation strategies.