论文标题
基于向量的表示，以增强头部姿势估计
A Vector-based Representation to Enhance Head Pose Estimation
论文作者
论文摘要
本文建议将旋转矩阵中的三个向量用作头部姿势估计中的表示形式，并基于此类表示的特征发展一个新的神经网络。我们解决当前头姿势估计工作中存在的两个潜在问题：1。用于头姿势估计的公共数据集使用欧拉角或四元组来注释数据样本。但是，这两种注释都存在不连续性问题，因此可能导致神经网络培训中的某些性能问题。 2。大多数研究工作报告欧拉角的绝对误差（MAE）是绩效的测量。我们表明，MAE可能不会反映实际行为，尤其是对于概况视图的情况。为了解决这两个问题，我们提出了一种新的注释方法，该方法使用三个向量来描述头部姿势和一个新的测量量值矢量的绝对误差（MAEV）来评估性能。我们还训练一个新的神经网络，以预测具有正交性限制的三个向量。我们提出的方法可以在AFLW2000和BIWI数据集上实现最先进的结果。实验表明，我们的基于向量的注释方法可以有效地减少大姿势角度的预测误差。
This paper proposes to use the three vectors in a rotation matrix as the representation in head pose estimation and develops a new neural network based on the characteristic of such representation. We address two potential issues existed in current head pose estimation works: 1. Public datasets for head pose estimation use either Euler angles or quaternions to annotate data samples. However, both of these annotations have the issue of discontinuity and thus could result in some performance issues in neural network training. 2. Most research works report Mean Absolute Error (MAE) of Euler angles as the measurement of performance. We show that MAE may not reflect the actual behavior especially for the cases of profile views. To solve these two problems, we propose a new annotation method which uses three vectors to describe head poses and a new measurement Mean Absolute Error of Vectors (MAEV) to assess the performance. We also train a new neural network to predict the three vectors with the constraints of orthogonality. Our proposed method achieves state-of-the-art results on both AFLW2000 and BIWI datasets. Experiments show our vector-based annotation method can effectively reduce prediction errors for large pose angles.
