作为计算机视觉中的一项重要任务,头部姿态估计在学术界和工业界都有广泛的应用。然而,在头部姿势估计领域仍然存在两个挑战:(1)即使给定相同的任务(例如,疲劳检测),现有的算法通常考虑三个角度的估计(即,roll,偏航,和间距)作为单独的方面,忽略它们的相互作用以及差异,因此所有层共享相同的参数;(2)角度估计的不连续性肯定会降低准确性。为了解决这两个问题,本研究提出了一种THESL-Net(具有自调整损耗网络的分层头部姿态估计)模型。具体来说,首先,提出了一种使用不同网络层的阶梯式估计的想法,在角度估计期间获得更大的自由度。此外,揭示了角度估计不连续的原因,不仅包括用四元数或欧拉角标记数据集,而且损失函数简单地增加了分类和回归损失。随后,对损失函数施加自调整约束,使角度估计更加一致。最后,为了检查不同角度范围对所提出的模型的影响,实验是在三个流行的公共基准数据集上进行的,BIWI,AFLW2000和UPNA,证明所提出的模型优于最先进的方法。
As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.