![]() However, these approaches oversimplify the rich visual input available to birds in their natural habitat, more so than for flying insects, since birds’ visual acuity and neural organisation is more complex (Altshuler & Srinivasan, 2018). For example, budgerigars flying through narrow corridors regulate flight speed in response to optic flow from sliding gratings projected onto the walls (Schiffner & Srinivasan, 2015). This proved useful as a first step in exploring how birds use visual self-motion cues, and in isolating their effects on flight control. Many of the previous works on avian visually guided flight followed insect studies (Baird et al., 2021 Tammero & Dickinson, 2002a, b Altshuler & Srinivasan, 2018) and investigated the animal’s behaviour in abstract visual environments (Bhagavatula et al., 2011 Schiffner & Srinivasan, 2015 Dakin et al., 2016 Ros & Biewener, 2016), such as corridors with vertically or horizontally striped walls. Much more is known about the role of vision in insect flight (Taylor et al., 2008), presumably because the size and sentience of birds complicates the experimental characterisation of their visuomotor control (Altshuler & Srinivasan, 2018). Nevertheless, the links between their vision, guidance and control are complex and poorly understood. With this approach, we provide a reproducible method that facilitates the collection of large volumes of data across many individuals, opening up new avenues for data-driven models of animal behaviour.įrom intercepting moving targets to manoeuvring through clutter, birds use vision to coordinate their flight manoeuvres with an agility and flexibility beyond the reach of current autonomous systems. ![]() We present pilot data from three sample flights: a pursuit flight, in which a hawk intercepts a moving target, and two obstacle avoidance flights. In contrast with previous approaches, our method allows us to consider different camera models and alternative gaze strategies for the purposes of hypothesis testing, allows us to consider visual input over the complete visual field of the bird, and is not limited by the technical specifications and performance of a head-mounted camera light enough to attach to a bird’s head in flight. ![]() Combining motion capture data from Harris’ hawks with a hybrid 3D model of the environment, we render RGB images, semantic maps, depth information and optic flow outputs that characterise the visual experience of the bird in flight. In this paper, we present a novel method that uses computer vision tools to analyse the role of active vision in bird flight, and demonstrate its use to answer behavioural questions. A better understanding of the role played by vision during these manoeuvres is not only relevant within the field of animal behaviour, but could also have applications for autonomous drones. Birds of prey rely on vision to execute flight manoeuvres that are key to their survival, such as intercepting fast-moving targets or navigating through clutter.
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