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透过鹰的视角:合成重建飞行中鸟类的视野

Through Hawks' Eyes: Synthetically Reconstructing the Visual Field of a Bird in Flight.

作者信息

Miñano Sofía, Golodetz Stuart, Cavallari Tommaso, Taylor Graham K

机构信息

Department of Biology, University of Oxford, 11a Mansfield Road, Oxford, OX1 3SZ UK.

Advanced Research Computing Centre, University College London, Gower Street, London WC1E 6BT, UK.

出版信息

Int J Comput Vis. 2023;131(6):1497-1531. doi: 10.1007/s11263-022-01733-2. Epub 2023 Mar 2.

DOI:10.1007/s11263-022-01733-2
PMID:37089199
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10110700/
Abstract

UNLABELLED

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. 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. 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. 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 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. We present pilot data from three sample flights: a pursuit flight, in which a hawk intercepts a moving target, and two obstacle avoidance flights. 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.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11263-022-01733-2.

摘要

未标注

猛禽依靠视觉来执行对其生存至关重要的飞行机动,例如拦截快速移动的目标或在复杂环境中导航。更好地理解视觉在这些机动过程中所起的作用不仅在动物行为领域具有重要意义,而且还可能应用于自主无人机。在本文中,我们提出了一种新颖的方法,该方法使用计算机视觉工具来分析主动视觉在鸟类飞行中的作用,并展示其用于回答行为问题的用途。我们将哈里斯鹰的运动捕捉数据与环境的混合3D模型相结合,生成RGB图像、语义地图、深度信息和光流输出,这些输出表征了飞行中鸟类的视觉体验。与以前的方法相比,我们的方法允许我们为假设检验考虑不同的相机模型和替代注视策略,允许我们考虑鸟类整个视野范围内的视觉输入,并且不受技术规格和足够轻以在飞行中附着在鸟头上的头戴式相机性能的限制。我们展示了来自三次样本飞行的初步数据:一次追击飞行,其中一只鹰拦截一个移动目标,以及两次避障飞行。通过这种方法,我们提供了一种可重复的方法,便于跨多个个体收集大量数据,为动物行为的数据驱动模型开辟了新途径。

补充信息

在线版本包含可在10.1007/s11263-022-01733-2获取的补充材料。

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