Automated bird flight pattern extraction and classification using machine learning
File(s)
Accepted version
Author(s)
Ostojic, Mili
Sethi, Sarab S
Type
Journal Article
Abstract
With bird populations across the world being impacted by ever-growing anthropogenic pressures, reliable
monitoring is essential to help halt or reverse declines. Existing visual bird monitoring approaches, which
employ cameras or radars to deliver automated and large-scale monitoring data, face a variety of issues.
Image-based species classification is only possible if the fine-scale features of a bird are clear, which can be
difficult to achieve in real monitoring contexts without expensive, high-resolution cameras due to occlusion
and lighting. Radar and video-based approaches which analyse longer-term flight behaviour over the course
of seconds can achieve more reliable results in real monitoring contexts, particularly from greater distances,
but still require expensive equipment and do not account for all the possible types of flight patterns. Here we
present a novel approach to track a wide range of bird flight patterns using easily acquired videos from
inexpensive, non-specialist cameras. As a proof-of-concept, we demonstrate how our approach can be used
to classify birds between four species, Red Kite, Kestrel, Black-Headed Gull and Sparrowhawk, which
represent four different types of flight patterns. The balanced accuracy of the classification is 0.5583, with a
recall and precision per species that range from 0.2640-0.7750 and 0.4583-0.5962, respectively. Our proof
of-concept study demonstrates how new and existing visual bird monitoring systems can leverage flight
patterns to deliver species-level insights at lower costs and on larger scales than before.
monitoring is essential to help halt or reverse declines. Existing visual bird monitoring approaches, which
employ cameras or radars to deliver automated and large-scale monitoring data, face a variety of issues.
Image-based species classification is only possible if the fine-scale features of a bird are clear, which can be
difficult to achieve in real monitoring contexts without expensive, high-resolution cameras due to occlusion
and lighting. Radar and video-based approaches which analyse longer-term flight behaviour over the course
of seconds can achieve more reliable results in real monitoring contexts, particularly from greater distances,
but still require expensive equipment and do not account for all the possible types of flight patterns. Here we
present a novel approach to track a wide range of bird flight patterns using easily acquired videos from
inexpensive, non-specialist cameras. As a proof-of-concept, we demonstrate how our approach can be used
to classify birds between four species, Red Kite, Kestrel, Black-Headed Gull and Sparrowhawk, which
represent four different types of flight patterns. The balanced accuracy of the classification is 0.5583, with a
recall and precision per species that range from 0.2640-0.7750 and 0.4583-0.5962, respectively. Our proof
of-concept study demonstrates how new and existing visual bird monitoring systems can leverage flight
patterns to deliver species-level insights at lower costs and on larger scales than before.
Date Acceptance
2026-07-17
Citation
Ecological Informatics
ISSN
1574-9541
Publisher
Elsevier
Journal / Book Title
Ecological Informatics
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted