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Uncertainty-Aware Gaze Tracking for Assisted Living Environments.

Paris Her, Logan Manderle, Philipe A Dias

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 7, 2023

Summary

This summary is machine-generated.

This study introduces a novel gaze tracking method for assisted living, using facial keypoints and uncertainty estimation for accurate, temporally stable predictions. The system enhances occupant interaction monitoring in multi-camera environments.

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Area of Science:

Computer Vision
Human-Computer Interaction
Assisted Living Technologies

Background:

Gaze direction is crucial for understanding occupant interaction in assisted living.
Accurate gaze tracking is challenging in multi-camera environments with occlusions and varied views.

Purpose of the Study:

To develop and evaluate a robust gaze tracking method for multi-camera assisted living settings.
To improve the accuracy and temporal stability of gaze estimation using neural networks and uncertainty quantification.

Main Methods:

A neural network regressor estimates gaze based on relative facial keypoint positions.
Uncertainty estimates from the regressor inform an angular Kalman filter for temporal integration.
Confidence gated units in the network mitigate uncertainties from partial occlusions and poor views.

Main Results:

The proposed method outperforms state-of-the-art gaze estimation techniques.
Uncertainty predictions correlate highly with actual angular error.
The system achieves accurate and temporally stable gaze predictions.

Conclusions:

The novel gaze tracking method offers significant improvements for assisted living environments.
Uncertainty estimation enhances the reliability of gaze tracking systems.
This technology can improve occupant monitoring and interaction analysis.