论文标题
在基于外观的凝视估计中朝着高性能的低复杂度校准
Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation
论文作者
论文摘要
来自RGB图像的基于外观的凝视估计提供了相对不受限制的凝视跟踪。我们以前提出了一种注视分解方法，该方法将目光的角度分解为与受试者无关的凝视估计和依赖受试者偏见的总和。本文扩展了该作用，以更完整的表征校准数据集的复杂性与估计精度之间的相互作用。我们使用新的Nislgaze数据集分析了凝视目标数量，凝视目标数量的影响以及校准数据中的头部位置的数量，该数据集非常适合分析这些效果，因为它在每个受试者的头部位置和方向上都有更多的多样性。对这些因素的更好理解可以使较低的复杂性高性能校准。我们的结果表明，仅使用单个凝视目标和单头部位置足以实现高质量的校准，超过最先进的方法超过6.3％。令人惊讶的发现之一是，相同的估计器在有和没有校准的情况下会产生最佳性能。为了更好地理解原因，我们提供了一种新的理论分析，该分析指定了可以预期的条件。
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.
