We propose a novel method to estimate a driver's points-of-gaze using a pair
of ordinary cameras mounted on the windshield and dashboard of a car. This is a
challenging problem due to the dynamics of traffic environments with 3D scenes
of unknown depths. This problem is further complicated by the volatile distance
between the driver and the camera system. To tackle these challenges, we
develop a novel convolutional network that simultaneously analyzes the image of
the scene and the image of the driver's face. This network has a camera
calibration module that can compute an embedding vector that represents the
spatial configuration between the driver and the camera system. This
calibration module improves the overall network's performance, which can be
jointly trained end to end.
We also address the lack of annotated data for training and evaluation by
introducing a large-scale driving dataset with point-of-gaze annotations. This
is an in situ dataset of real driving sessions in an urban city, containing
synchronized images of the driving scene as well as the face and gaze of the
driver. Experiments on this dataset show that the proposed method outperforms
various baseline methods, having the mean prediction error of 29.69 pixels,
which is relatively small compared to the $1280{\times}720$ resolution of the
scene camera.

我们提出了一种使用安装在车辆挡风玻璃和仪表盘上的普通摄像头来估计驾驶员凝视点的新方法。该方法通过开发一种卷积网络来同时分析场景图像和驾驶员面部图像，其中包含一个相机校准模块，可以计算表示驾驶员和摄像头系统之间空间配置的嵌入向量，该模块改善了网络的性能并可以进行端到端的联合训练。我们还引入了一种大规模的驾驶数据集，其中包含了真实驾驶情景的图像以及驾驶员面部和凝视数据，对该数据集的实验表明，所提出的方法优于各种基准方法，均方误差为 29.69 像素，相对于场景摄像头的 $1280 {imes} 720$ 分辨率而言，误差较小。

驾驶员注意力跟踪和分析

Driver Attention Tracking and Analysis

We study human pose estimation in extremely low-light images. This task is
challenging due to the difficulty of collecting real low-light images with
accurate labels, and severely corrupted inputs that degrade prediction quality
significantly. To address the first issue, we develop a dedicated camera system
and build a new dataset of real low-light images with accurate pose labels.
Thanks to our camera system, each low-light image in our dataset is coupled
with an aligned well-lit image, which enables accurate pose labeling and is
used as privileged information during training. We also propose a new model and
a new training strategy that fully exploit the privileged information to learn
representation insensitive to lighting conditions. Our method demonstrates
outstanding performance on real extremely low light images, and extensive
analyses validate that both of our model and dataset contribute to the success.

本文研究了极低光照下的人体姿态估计，通过自主构建专用相机系统并建立包含准确姿态数据的新数据集，并使用新模型和新训练策略，该系统在真实极低光照环境下成功实现了姿态估计。