Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation

• URL: http://arxiv.org/abs/2001.09284v2
• Date: Sun, 13 Feb 2022 14:58:42 GMT
• Title: Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation
• Authors: Zhaokang Chen and Bertram E. Shi
• Abstract summary: Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking. We analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data. Using only a single gaze target and single head position is sufficient to achieve high quality calibration, outperforming state-of-the-art methods by more than 6.3%.
• Score: 7.857571508499849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking. We have previously proposed a gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. This paper extends that work with a more complete characterization of the interplay between the complexity of the calibration dataset and estimation accuracy. We analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data using a new NISLGaze dataset, which is well suited for analyzing these effects as it includes more diversity in head positions and orientations for each subject than other datasets. A better understanding of these factors enables low complexity high performance calibration. Our results indicate that using only a single gaze target and single head position is sufficient to achieve high quality calibration, outperforming state-of-the-art methods by more than 6.3%. One of the surprising findings is that the same estimator yields the best performance both with and without calibration. To better understand the reasons, we provide a new theoretical analysis that specifies the conditions under which this can be expected.

Related papers

• Data Augmentation via Latent Diffusion for Saliency Prediction [67.88936624546076]
  Saliency prediction models are constrained by the limited diversity and quantity of labeled data. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
  arXiv  Detail & Related papers  (2024-09-11T14:36:24Z)
• Generalized Maximum Likelihood Estimation for Perspective-n-Point Problem [11.662329798571953]
  Existing methods ignore the anisotropy uncertainty of observations, as demonstrated in several real-world datasets in this paper. We propose a generalized maximum likelihood solver, named GML, that minimizes the criterion by it achieves better translation accuracy. It is more accurate under very noisy observations in vision-based UAV task, outperforming the best baseline by 34.4% in translation estimation.
  arXiv  Detail & Related papers  (2024-08-04T07:06:04Z)
• NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation [37.977032771941715]
  We propose a novel Head-Eye redirection parametric model based on Neural Radiance Field. Our model can decouple the face and eyes for separate neural rendering. It can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction.
  arXiv  Detail & Related papers  (2022-12-30T13:52:28Z)
• One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with Single Encoders [0.0]
  We propose a gaze estimation model that implements the ResNet and Inception model architectures and makes predictions using only one eye image. With the use of lightweight architectures, we achieve high performance on the GazeCapture dataset with very low model parameter counts. We also notice significantly lower errors on the right eye images in the test set, which could be important in the design of future gaze estimation-based tools.
  arXiv  Detail & Related papers  (2022-11-22T01:12:31Z)
• Contrastive Weighted Learning for Near-Infrared Gaze Estimation [0.228438857884398]
  This paper proposes GazeCWL, a novel framework for gaze estimation with near-infrared images using contrastive learning. Our model outperforms previous domain generalization models in infrared image based gaze estimation.
  arXiv  Detail & Related papers  (2022-11-06T10:03:23Z)
• Visualizing and Alleviating the Effect of Radial Distortion on Camera Calibration Using Principal Lines [8.051200635659006]
  It is suggested that the estimation of principal point should based on linearly independent pairs of nearly parallel principal lines. Experimental results show that more robust and consistent calibration results for the foregoing estimation can actually be obtained.
  arXiv  Detail & Related papers  (2022-06-28T17:21:26Z)
• The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions [53.478856119297284]
  We introduce the probabilistic normal epipolar constraint (PNEC) that overcomes the limitation by accounting for anisotropic and inhomogeneous uncertainties in the feature positions. In experiments on synthetic data, we demonstrate that the novel PNEC yields more accurate rotation estimates than the original NEC. We integrate the proposed method into a state-of-the-art monocular rotation-only odometry system and achieve consistently improved results for the real-world KITTI dataset.
  arXiv  Detail & Related papers  (2022-04-05T14:47:11Z)
• A Model for Multi-View Residual Covariances based on Perspective Deformation [88.21738020902411]
  We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
  arXiv  Detail & Related papers  (2022-02-01T21:21:56Z)
• Expected Validation Performance and Estimation of a Random Variable's Maximum [48.83713377993604]
  We analyze three statistical estimators for expected validation performance. We find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias. We find that the two biased estimators lead to the fewest incorrect conclusions.
  arXiv  Detail & Related papers  (2021-10-01T18:48:47Z)
• Towards Top-Down Just Noticeable Difference Estimation of Natural Images [65.14746063298415]
  Just noticeable difference (JND) estimation mainly dedicates to modeling the visibility masking effects of different factors in spatial and frequency domains. In this work, we turn to a dramatically different way to address these problems with a top-down design philosophy. Our proposed JND model can achieve better performance than several latest JND models.
  arXiv  Detail & Related papers  (2021-08-11T06:51:50Z)
• ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation [52.5465548207648]
  ETH-XGaze is a new gaze estimation dataset consisting of over one million high-resolution images of varying gaze under extreme head poses. We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.
  arXiv  Detail & Related papers  (2020-07-31T04:15:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.