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 ..., 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., , # Data and code to reproduce \"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\" by **Michael Chimento**, **Alex Hoi Hang Chan**, Lucy M. Aplin & Fumihiro Kano. **Bold denotes co-first authorship**. Note: This is separate from the code necessary to run 3D-SOCS yourself, which can be found at [this github repository](https://github.com/alexhang212/3D-SOCS).

3D tracking pipeline and system accuracy validation were performed in Python, and any questions related to these should be directed to Alex Chan (hoi-hang.chan at uni-konstanz.de). Bayesian statistical analysis, figures and tables were all performed in R, and any questions related to these (or the Python scripts that control the data collection system) should be directed to Michael Chimento (mchimento at ab.mpg.de). We provide required pa...,