Chimento, M.; Chan, A. H. H.; Aplin, L. M.; Kano, F.

Collection of large behavioral data-sets on wild animals in natural habitats is vital in ecology and evolution studies. Recent progress in machine learning and computer vision, combined with inexpensive microcomputers, have unlocked a new frontier of fine-scale markerless measurements. Here, we leverage these advancements to develop a 3D Synchronized Outdoor Camera System (3D-SOCS): an inexpensive, mobile and automated method for collecting behavioral data on wild animals using synchronized video frames from Raspberry Pi controlled cameras. Accuracy tests demonstrate 3D-SOCS\' markerless tracking can estimate postures with a 3mm tolerance. To illustrate its research potential, we place 3D-SOCS in the field and conduct a stimulus presentation experiment. We estimate 3D postures and trajectories for multiple individuals of different bird species, and use this data to characterize the visual field configuration of wild great tits (Parus major), a model species in behavioral ecology. We find their optic axes at approximately {+/-}60{degrees} azimuth and -5{degrees} elevation. Furthermore, birds exhibit individual differences in lateralization. We also show that birds\' convex hulls predicts body weight, highlighting 3D-SOCS\' potential for non-invasive population monitoring. In summary, 3D-SOCS is a first-of-its-kind camera system for wild research, presenting exciting potential to measure fine-scaled behaviour and morphology in wild birds.