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DriveAHead — A Large-Scale Driver Head Pose Dataset

机译：Driveahead - 一个大型驱动头姿势数据集

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Head pose monitoring is an important task for driver assistance systems, since it is a key indicator for human attention and behavior. However, current head pose datasets either lack complexity or do not adequately represent the conditions that occur while driving. Therefore, we introduce DriveAHead, a novel dataset designed to develop and evaluate head pose monitoring algorithms in real driving conditions. We provide frame-by-frame head pose labels obtained from a motion-capture system, as well as annotations about occlusions of the driver's face. To the best of our knowledge, DriveAHead is the largest publicly available driver head pose dataset, and also the only one that provides 2D and 3D data aligned at the pixel level using the Kinect v2. Existing performance metrics are based on the mean error without any consideration of the bias towards one position or another. Here, we suggest a new performance metric, named Balanced Mean Angular Error, that addresses the bias towards the forward looking position existing in driving datasets. Finally, we present the Head Pose Network, a deep learning model that achieves better performance than current state-of-the-art algorithms, and we analyze its performance when using our dataset.
机译：头部姿势监测是驾驶员辅助系统的重要任务，因为它是人类注意力和行为的关键指标。然而，当前的头部姿势数据集缺乏复杂性，或者不充分代表驾驶时发生的条件。因此，我们介绍了Driveahead，这是一个旨在在实际驾驶条件下开发和评估头部姿势监测算法的新型数据集。我们提供从运动捕获系统获得的逐帧头部姿势标签，以及关于驾驶员脸部的闭塞的注释。据我们所知，Driveahead是最大的公共驱动程序头部姿势数据集，也是仅使用Kinect V2提供在像素级别在像素级别对齐的2D和3D数据的唯一一个。现有的性能指标基于平均误差而不考虑偏向一个位置或另一个位置。在这里，我们建议一个新的性能度量，命名为平衡平均角误差，该误差是朝向在驾驶数据集中存在的前向上的位置的偏置。最后，我们介绍了头部姿势网络，一个深入的学习模型，实现比当前最先进的算法更好的性能，并且我们在使用我们的数据集时分析其性能。

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