An advanced driving assistant system is one of the most popular topics nowadays, and depth estimation is an important cue for advanced driving assistant system. Depth prediction is a key problem in understanding the geometry of a road scene for advanced driving assistant system. In comparison to other depth estimation methods using stereo depth perception, determining depth relation using a monocular camera is considerably challenging. In this article, a fully convolutional neural network with skip connection based on a monocular video sequence is proposed. With the integration framework that combines skip connection, fully convolutional network and the consistency between consecutive frames of the input sequence, high-resolution depth maps are obtained with lightweight network training and fewer computations. The proposed method models depth estimation as a regression problem and trains the proposed network using a scale invariance optimization based on L2 loss function, which measures the relationships between points in the consecutive frames. The proposed method can be used for depth estimation of a road scene without the need for any extra information or geometric priors. Experiments on road scene data sets demonstrate that the proposed approach outperforms previous methods for monocular depth estimation in dynamic scenes. Compared with the currently proposed method, our method has achieved good results when using the Eigen split evaluation method. The obvious prominent one is that the linear root mean squared error result is 3.462 and the δ < 1.25 result is 0.892.

中文翻译：

---

基于全卷积神经网络的单目图像序列道路场景深度估计

高级驾驶辅助系统是当今最热门的话题之一，而深度估计是高级驾驶辅助系统的重要线索。深度预测是高级驾驶辅助系统理解道路场景几何形状的关键问题。与使用立体深度感知的其他深度估计方法相比，使用单目相机确定深度关系相当具有挑战性。在本文中，提出了一种基于单目视频序列的具有跳跃连接的全卷积神经网络。通过结合跳过连接、全卷积网络和输入序列连续帧之间的一致性的集成框架，通过轻量级网络训练和更少的计算获得高分辨率的深度图。所提出的方法将深度估计建模为回归问题，并使用基于 L2 损失函数的尺度不变性优化来训练所提出的网络，该函数测量连续帧中点之间的关系。所提出的方法可用于道路场景的深度估计，而无需任何额外信息或几何先验。在道路场景数据集上的实验表明，所提出的方法优于先前在动态场景中进行单目深度估计的方法。与目前提出的方法相比，我们的方法在使用特征分割评估方法时取得了良好的效果。明显突出的是线性均方根误差结果为3.462，δ < 1.25结果为0.892。