Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary
Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary
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DOI：
10.1007/s11263-023-01768-z
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发表时间：
2023-03-06
影响因子：
19.5
通讯作者：
Badger，Marc
中科院分区：
文献类型：
--
作者：
Xiao，Shiting;Wang，Yufu;Badger，Marc
捕捉社会群体中个体之间详细互动的能力是我们研究动物行为和神经科学的基础。深度学习和计算机视觉的最新进展正在推动可以同时记录多个人的动作和交互的方法的快速发展。然而，许多社会性物种，如鸟类，深深地生活在三维世界中。这个世界引入了额外的感知挑战，例如遮挡，方向依赖性外观，表观尺寸的大变化以及3D重建的传感器覆盖范围差，这些都是研究仅在2D平面上移动和交互的动物的应用程序所不会遇到的。在这里，我们介绍了一个系统，用于研究一组鸣禽的行为动力学，因为他们在整个3D鸟舍移动。我们研究了在三维空间中跟踪一组密切互动的动物时所产生的复杂性，并介绍了一种用于评估多视图跟踪器的新数据集。最后，我们分析捕获的行为图数据，并表明，社会背景影响鸟舍中鸟类之间的顺序相互作用的分布。
The ability to capture detailed interactions among individuals in a social group is foundational to our study of animal behavior and neuroscience. Recent advances in deep learning and computer vision are driving rapid progress in methods that can record the actions and interactions of multiple individuals simultaneously. Many social species, such as birds, however, live deeply embedded in a three-dimensional world. This world introduces additional perceptual challenges such as occlusions, orientation-dependent appearance, large variation in apparent size, and poor sensor coverage for 3D reconstruction, that are not encountered by applications studying animals that move and interact only on 2D planes. Here we introduce a system for studying the behavioral dynamics of a group of songbirds as they move throughout a 3D aviary. We study the complexities that arise when tracking a group of closely interacting animals in three dimensions and introduce a novel dataset for evaluating multi-view trackers. Finally, we analyze captured ethogram data and demonstrate that social context affects the distribution of sequential interactions between birds in the aviary.