activity
20162026
most citedSeeing biodiversity: perspectives in machine learning for wildlife conservation

733 citations · 1.3k across the 48 of their papers we have counts for

collaborators

51 papers

cs.CV2026

Benchmarking NACTI Species Recognition in Long-Tailed Regimes

Zehua Liu, Tilo Burghardt

As with most ``in the wild'' collections of the natural world, the North America Camera Trap Images (NACTI) dataset exhibits long-tailed class imbalance, with the largest class cov…

cs.CV2026

The PanAf-SBR Dataset: Social Behaviour Recognition for Wild Great Apes

Maciej Braszczok, Otto Brookes, Xiaoxuan Ma +6

Behavioural shifts in wild great ape populations, particularly the breakdown of social structures, can serve as an early indicator of population decline. Automating the detection o…

cs.CV2026

Automating Visual Recognition of Leprosy in Wild Chimpanzees

Katie I. Murray, Anna C. Bowland, Marina Ramon +9

Leprosy (Mycobacterium leprae) has been confirmed in wild western chimpanzees (Pan troglodytes verus) in West Africa, presenting as clear and progressive visual symptoms. Manual re…

cs.CV2026

CoralLite: μCT Reconstruction of Coral Colonies from Individual Corallites

Jess Jones, Leonardo Bertini, Kenneth Johnson +2

The life history of an individual coral is archived within the accreting skeleton of the colony. While reef-forming coral colonies (e.g. massive sp.) may live fo…

q-bio.PE2026

Centering Ecological Goals in Automated Identification of Individual Animals

Lukas Picek, Timm Haucke, Lukáš Adam +16

Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent…

cs.CV2026

Automated Re-Identification of Holstein-Friesian Cattle in Dense Crowds

Phoenix Yu, Tilo Burghardt, Andrew W Dowsey +1

Holstein-Friesian detection and re-identification (Re-ID) methods capture individuals well when targets are spatially separate. However, existing approaches, including YOLO-based s…

cs.CV2026

Deep in the Jungle: Towards Automating Chimpanzee Population Estimation

Tom Raynes, Otto Brookes, Timm Haucke +6

The estimation of abundance and density in unmarked populations of great apes relies on statistical frameworks that require animal-to-camera distance measurements. In practice, acq…

cs.CV2025

Visual-textual Dermatoglyphic Animal Biometrics: A First Case Study on Panthera tigris

Wenshuo Li, Majid Mirmehdi, Tilo Burghardt

Biologists have long combined visuals with textual field notes to re-identify (Re-ID) animals. Contemporary AI tools automate this for species with distinctive morphological featur…

cs.CV2025

The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification

Dante Francisco Wasmuht, Otto Brookes, Maximillian Schall +21

Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual r…

cs.CV2025

Long-tailed Species Recognition in the NACTI Wildlife Dataset

Zehua Liu, Tilo Burghardt

As most ''in the wild'' data collections of the natural world, the North America Camera Trap Images (NACTI) dataset shows severe long-tailed class imbalance, noting that the larges…

cs.CV2025

WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs

Nguyen Ngoc Dat, Tom Richardson, Matthew Watson +7

Live tracking of wildlife via high-resolution video processing directly onboard drones is widely unexplored and most existing solutions rely on streaming video to ground stations t…

cs.CV2025★ 1 cited

Towards Application-Specific Evaluation of Vision Models: Case Studies in Ecology and Biology

Alex Hoi Hang Chan, Otto Brookes, Urs Waldmann +11

Computer vision methods have demonstrated considerable potential to streamline ecological and biological workflows, with a growing number of datasets and models becoming available…

cs.CV2025

Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation

Chengxi Zeng, Yuxuan Jiang, Fan Zhang +2

The deployment of foundation models for medical imaging has demonstrated considerable success. However, their training overheads associated with downstream tasks remain substantial…

cs.CV2025

MMLA: Multi-Environment, Multi-Species, Low-Altitude Drone Dataset

Jenna Kline, Samuel Stevens, Guy Maalouf +10

Real-time wildlife detection in drone imagery supports critical ecological and conservation monitoring. However, standard detection models like YOLO often fail to generalize across…

cs.CV2025

The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition

Otto Brookes, Maksim Kukushkin, Majid Mirmehdi +17

Computer vision analysis of camera trap video footage is essential for wildlife conservation, as captured behaviours offer some of the earliest indicators of changes in population…

cs.CV2025

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations

Chengxi Zeng, David Smithard, Alberto M Gambaruto +1

Vision foundation models have demonstrated exceptional generalization capabilities in segmentation tasks for both generic and specialized images. However, a performance gap persist…

cs.CV2024★ 18 cited

Holstein-Friesian Re-Identification using Multiple Cameras and Self-Supervision on a Working Farm

Phoenix Yu, Tilo Burghardt, Andrew W Dowsey +1

We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their uniq…

cs.CV2024

ChimpVLM: Ethogram-Enhanced Chimpanzee Behaviour Recognition

Otto Brookes, Majid Mirmehdi, Hjalmar Kuhl +1

We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail spe…

cs.CV2024★ 1 cited

Universal Bovine Identification via Depth Data and Deep Metric Learning

Asheesh Sharma, Lucy Randewich, William Andrew +7

This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associate…

cs.LG2024★ 2 cited

RBF-PINN: Non-Fourier Positional Embedding in Physics-Informed Neural Networks

Chengxi Zeng, Tilo Burghardt, Alberto M Gambaruto

While many recent Physics-Informed Neural Networks (PINNs) variants have had considerable success in solving Partial Differential Equations, the empirical benefits of feature mappi…

cs.LG2024★ 1 cited

Feature Mapping in Physics-Informed Neural Networks (PINNs)

Chengxi Zeng, Tilo Burghardt, Alberto M Gambaruto

In this paper, the training dynamics of PINNs with a feature mapping layer via the limiting Conjugate Kernel and Neural Tangent Kernel is investigated, shedding light on the conver…

cs.CV2024★ 3 cited

PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition

Otto Brookes, Majid Mirmehdi, Colleen Stephens +24

We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frame…

cs.CV2023★ 1 cited

Diving with Penguins: Detecting Penguins and their Prey in Animal-borne Underwater Videos via Deep Learning

Kejia Zhang, Mingyu Yang, Stephen D. J. Lang +3

African penguins (Spheniscus demersus) are an endangered species. Little is known regarding their underwater hunting strategies and associated predation success rates, yet this is…

cs.CV2023

Deep Visual-Genetic Biometrics for Taxonomic Classification of Rare Species

Tayfun Karaderi, Tilo Burghardt, Raphael Morard +1

Visual as well as genetic biometrics are routinely employed to identify species and individuals in biological applications. However, no attempts have been made in this domain to co…