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Tilo Burghardt

6 accepted papers

2026

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

Dante Wasmuht, Otto Brookes, Maximilian Schall, Pablo Palencia, Christopher Beirne, Tilo Burghardt +18
CVPR 2026

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 re-identification and behavior recognition. However, existing datasets are limited in scale, constrained to a few species

Cited by 0Source ↗Scholar ↗
2025

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

Otto Brookes, Maksim Kukushkin, Majid Mirmehdi, Colleen Stephens, Paula Dieguez, Thurston C. Hicks +14
CVPR 2025award

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 health. Recently, several high-impact animal behaviour datasets and methods have been introduced to encourage their use;…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

Use Your Head: Improving Long-Tail Video Recognition

Toby Perrett, Saptarshi Sinha, Tilo Burghardt, Majid Mirmehdi, Dima Damen
CVPR 2023poster

This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2021

Temporal-Relational CrossTransformers for Few-Shot Action Recognition

Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen
CVPR 2021poster

We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous few-shot works, we construct class prototypes using the CrossTransformer attention mechanism to observe relevant sub-seque…

Cited by 215PDF ↗code ↗Scholar ↗
2019

Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference

William Andrew, Colin Greatwood, Tilo Burghardt
IROS 2019poster

This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in freely moving herds. We…

Cited by 87Source ↗Scholar ↗
2018

Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision

William Andrew, Colin Greatwood, Tilo Burghardt
IROS 2018poster

This paper discusses deep learning for solving static and dynamic search and recovery tasks - such as the retrieval of all instances of actively moving targets - based on partial-view Unmanned Aerial Vehicle (UAV)-like sensing. In particular, we demonstrate that abstracted tactic and strategic explo…

Cited by 13Source ↗Scholar ↗