Data and code from: 3D-SOCS: synchronized video capture for posture estimation

This repository provides the data and code necessary to reproduce the
manuscript "Peering into the world of wild passerines with 3D-SOCS:
synchronized video capture for posture estimation".This repository
also contains sample datasets for running the code and bounding box and
keypoint annotations. 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◦ azimuth and −5◦ elevation.
Furthermore, birds exhibit functional lateralization in their use of the
right eye with conspecific stimulus, and show individual differences in
lateralization. We also show that birds’ convex hulls predicts body
weight, highlighting 3D-SOCS’ potential for non-invasive population
monitoring. 3D-SOCS is a first-of-its-kind camera system for wild
research, presenting exciting potential to measure fine-scaled behavior
and morphology in wild birds.