Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA.
Centre for Ecology and Conservation, University of Exeter, Penryn, UK.
J R Soc Interface. 2018 Oct 24;15(147):20180653. doi: 10.1098/rsif.2018.0653.
Tracking the movements of birds in three dimensions is integral to a wide range of problems in animal ecology, behaviour and cognition. Multi-camera stereo-imaging has been used to track the three-dimensional (3D) motion of birds in dense flocks, but precise localization of birds remains a challenge due to imaging resolution in the depth direction and optical occlusion. This paper introduces a portable stereo-imaging system with improved accuracy and a simple stereo-matching algorithm that can resolve optical occlusion. This system allows us to decouple body and wing motion, and thus measure not only velocities and accelerations but also wingbeat frequencies along the 3D trajectories of birds. We demonstrate these new methods by analysing six flocking events consisting of 50 to 360 jackdaws () and rooks () as well as 32 jackdaws and 6 rooks flying in isolated pairs or alone. Our method allows us to (i) measure flight speed and wingbeat frequency in different flying modes; (ii) characterize the U-shaped flight performance curve of birds in the wild, showing that wingbeat frequency reaches its minimum at moderate flight speeds; (iii) examine group effects on individual flight performance, showing that birds have a higher wingbeat frequency when flying in a group than when flying alone and when flying in dense regions than when flying in sparse regions; and (iv) provide a potential avenue for automated discrimination of bird species. We argue that the experimental method developed in this paper opens new opportunities for understanding flight kinematics and collective behaviour in natural environments.
追踪鸟类在三维空间中的运动是动物生态学、行为学和认知学中一系列问题的基础。多摄像机立体成像已被用于追踪密集鸟群的三维（3D）运动，但由于深度方向的成像分辨率和光学遮挡，鸟类的精确定位仍然是一个挑战。本文介绍了一种具有改进精度的便携式立体成像系统和一种简单的立体匹配算法，可以解决光学遮挡问题。该系统允许我们解耦身体和翅膀运动，从而不仅可以测量速度和加速度，还可以沿鸟类的 3D 轨迹测量翅膀的拍打频率。我们通过分析六个由 50 到 360 只寒鸦（）和松鸦（）以及 32 只寒鸦和 6 只松鸦组成的集群事件以及 32 只寒鸦和 6 只松鸦的单独对或单独飞行来演示这些新方法。我们的方法可以：（i）在不同的飞行模式下测量飞行速度和翅膀拍打频率；（ii）在野外描述鸟类 U 形飞行性能曲线，表明翅膀拍打频率在中等飞行速度下达到最小值；（iii）研究群体对个体飞行性能的影响，表明鸟类在群体中飞行时的翅膀拍打频率高于单独飞行时，在密集区域飞行时高于在稀疏区域飞行时；（iv）为自动区分鸟类物种提供了一种潜在的方法。我们认为，本文中开发的实验方法为理解自然环境中的飞行运动学和集体行为开辟了新的机会。
