gaze-behavior-annotation-toolkit

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:

1. Post-hoc synchronization across multiple video streams
2. Semi-automatic annotation of gaze target categories
3. 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