Data supporting: Automated Object Detection in Mobile Eye-Tracking Research: Comparing Manual Coding with Tag Detection, Shape Detection, Matching, and Machine Learning

The goal of the current study is to compare the different methods for automated object detection (i.e., tag detection, shape detection, matching, and machine learning) with manual coding on different types of objects (i.e., static, dynamic, and dynamic with human interaction) and describe the advantages and limitations of each method. We tested the methods in an experiment that utilizes mobile eye tracking because of the importance of attention in communication science and the challenges this type of data poses to analyze different objects because visual parameters are consistently changing within and between participants. Python scripts, processed videos, R scripts, and processed data files are included for each method.

本研究旨在对比不同自动化目标检测（automated object detection）方法——包括标签检测（tag detection）、形状检测（shape detection）、匹配（matching）与机器学习（machine learning）——与手动编码（manual coding）方法在不同类型目标（静态目标、动态目标以及带人类交互的动态目标）上的应用效果，并阐明各方法的优势与局限。鉴于注意在传播学（communication science）中的重要性，且由于不同被试内部及被试间的视觉参数持续变化，此类数据在分析不同目标时存在诸多挑战，因此本研究在一项采用移动眼动追踪（mobile eye tracking）的实验中对上述方法进行了测试。本研究附带各方法对应的Python脚本、经处理的视频、R脚本以及处理后的数据文件。