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Related Experiment Video

Updated: Oct 5, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Gaze Tracking Based on Concatenating Spatial-Temporal Features.

Bor-Jiunn Hwang¹, Hui-Hui Chen¹, Chaur-Heh Hsieh²

¹Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

This summary is machine-generated.

This study introduces a novel CNN-LSTM model for gaze point estimation using video data, significantly improving accuracy by integrating spatial and temporal features. The developed method enhances prediction performance for visual behavior analysis.

Keywords:

convolutional neural network (CNN)deep learning gaze tracking long short-term memory (LSTM)

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Area of Science:

Computer Vision
Human-Computer Interaction
Machine Learning

Background:

Gaze point estimation traditionally uses static images, neglecting temporal dynamics.
Sequence relationships in visual behavior data are crucial for accurate gaze estimation.

Purpose of the Study:

To improve gaze point estimation accuracy by incorporating temporal features from video data.
To develop a model that effectively captures both spatial and temporal information.

Main Methods:

Proposed a Convolutional Neural Network Concatenating Long Short-Term Memory network (CCLN) model.
Utilized videos as input data to capture spatial (CNN) and temporal (LSTM) features.
Investigated optimization strategies including LSTM layers, batch normalization, and global average pooling.

Main Results:

The CCLN model demonstrated superior prediction accuracy compared to existing CNN-based methods.
Achieved 93.1% accuracy with the optimized CCLN model.
Evaluated dataset construction methods and the impact of transfer learning.

Conclusions:

Integrating temporal features from video data significantly enhances gaze point estimation.
The proposed CCLN model offers a robust approach for time-series video-based gaze analysis.
The study provides a framework for dataset creation and model optimization in gaze estimation research.