| name | gaze-behavior-annotation-toolkit |
| description | AI-powered deep-learning toolkit for automatic annotation of egocentric eye-tracking and video data of child-caregiver interaction. Supports post-hoc video synchronization, semi-automatic gaze target categorization, and behavioral coding of poses and hand actions. Use for developmental psychology, eye-tracking analysis, behavioral video coding, and caregiver-infant interaction studies. |
GazeBehavior Annotation Toolkit (GBAT)
Methodology from arXiv:2605.22962 (May 2026). Submitted to IEEE ICDL 2026.
Overview
GBAT is a deep-learning-based toolkit designed to automate the annotation pipeline for egocentric eye-tracking and video data of child-caregiver interactions. It addresses three key preprocessing and feature extraction challenges:
- Post-hoc synchronization across multiple video streams
- Semi-automatic annotation of gaze target categories
- Categorization of participants' poses and hand actions
Key Features
- Deep learning models for gaze target detection from egocentric video
- Automatic synchronization of multiple camera streams
- Pose and hand action classification
- Scalable for large-scale and longitudinal developmental studies
- Built on modern computer vision architectures
Applications
- Investigating attentional dynamics in naturalistic behavior
- Studying how attention interacts with action and language in real time
- Large-scale developmental psychology research
- Longitudinal studies of early human development
Trigger Words
- gaze behavior, eye tracking, egocentric video, child-caregiver interaction
- behavioral annotation, video synchronization, pose estimation
- developmental psychology, attention dynamics, naturalistic behavior
- GBAT, annotation toolkit
Related Skills
- behavior-vlm-neuroscience
