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.

Methods
We develop and use a markerless 3D tracking system to estimate the posture of wild passerine birds (great tits and blue tits) in the field. We demonstrate the capabilities of this system using a stimulus-display experiment. 3D tracking pipeline and system accuracy validation were performed in Python, and any questions related to these should be directed to Alex Chan. Bayesian statistical analysis, figures and tables were all peformed in R, and any questions related to these, along with those related to the Python scripts that control the Raspberry Pis should be directed to Michael Chimento. We provide required packages, directory contents and column descriptions for all analyses below.