Abstract. Head pose estimation is a typical computer vision task that has been applied to many helpful applications. It is still challenging due to occlusions, low resolution, and extreme pose changes. A method is proposed for landmark-free head pose estimation in a fusion inception deep neural network. The fusion inception network is developed to predict head pose angles without landmarks in RGB images only, which avoids the error caused by landmark location faults. A three-channel prediction module in the developed network is designed to perform classification and regression jointly. A data augmentation method is proposed for sample expansion to alleviate overfitting. A balance loss strategy is proposed to take place of cross-entropy for pose angle classification. The developed balance loss can be applied to deal with class imbalance. The proposed method has excellent performance in head pose estimation by comparison with state-of-the-art methods on some challenging datasets.

中文翻译：

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使用融合初始深度神经网络进行无地标头部姿态估计

摘要。头部姿势估计是一项典型的计算机视觉任务，已应用于许多有用的应用程序。由于遮挡、低分辨率和极端姿势变化，它仍然具有挑战性。提出了一种在融合初始深度神经网络中进行无地标头部姿势估计的方法。融合初始网络被开发用于仅在 RGB 图像中预测没有地标的头部姿态角度，避免了由地标定位错误引起的误差。开发的网络中的三通道预测模块旨在联合执行分类和回归。提出了一种用于样本扩展的数据增强方法，以减轻过拟合。提出了一种平衡损失策略来代替交叉熵进行姿势角分类。开发的平衡损失可用于处理类别不平衡。